import os
from utils import DotDict, namedtuple_with_defaults, zip_namedtuple, config_as_dict
RandCropper = namedtuple_with_defaults(
'RandCropper', 'min_crop_scales, max_crop_scales, \
min_crop_aspect_ratios, max_crop_aspect_ratios, \
min_crop_overlaps, max_crop_overlaps, \
min_crop_sample_coverages, max_crop_sample_coverages, \
min_crop_object_coverages, max_crop_object_coverages, \
max_crop_trials', [0.0, 1.0, 0.5, 2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 25])
RandPadder = namedtuple_with_defaults(
'RandPadder', 'rand_pad_prob, max_pad_scale, fill_value', [0.0, 1.0, 127])
ColorJitter = namedtuple_with_defaults(
'ColorJitter', 'random_hue_prob, max_random_hue, \
random_saturation_prob, max_random_saturation, \
random_illumination_prob, max_random_illumination, \
random_contrast_prob, max_random_contrast',
[0.0, 18, 0.0, 32, 0.0, 32, 0.0, 0.5])
cfg = DotDict()
cfg.ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
# training configs
cfg.train = DotDict()
# random cropping samplers
cfg.train.rand_crop_samplers = [
RandCropper(min_crop_scales=0.3, min_crop_overlaps=0.1),
RandCropper(min_crop_scales=0.3, min_crop_overlaps=0.3),
RandCropper(min_crop_scales=0.3, min_crop_overlaps=0.5),
def exportHistoryData(self, exportType):
import flask
import csv
import StringIO
import re
from utils import namedtuple_with_defaults, prepare_dict, load_json, rename_duplicates
history_dicts = self._getHistoryDicts()
if history_dicts is not None:
si = StringIO.StringIO()
headers = [
'File name', 'Timestamp', 'Success', 'Print time',
'Filament length', 'Filament volume'
]
fields = [
'fileName', 'timestamp', 'success', 'printTime', 'filamentLength',
'filamentVolume'
]
if exportType == 'csv':
writer = csv.writer(si, quoting=csv.QUOTE_ALL)
writer.writerow(headers)
for historyDetails in history_dicts:
output = list()
for field in fields:
value = historyDetails.get(field, '-')
output.append(value if value is not None else '-')
writer.writerow(output)
response = flask.make_response(si.getvalue())
response.headers["Content-type"] = "text/csv"
response.headers[
"Content-Disposition"] = "attachment; filename=octoprint_print_history_export.csv"
elif exportType == 'csv_extra':
unused_fields = ["spool", "user", "note", "id", "parameters"]
csv_header = set(fields)
for historyDetails in history_dicts:
parameters = load_json(historyDetails, "parameters")
csv_header |= set(parameters.keys())
# Doesn't handle Camelcase
csv_header = map(lambda x: x.replace(" ", "_"), csv_header)
csv_header = rename_duplicates(fields, csv_header, prefix="g")
rearranged_header = fields[:]
for column in csv_header:
if column not in headers:
rearranged_header.append(column)
csv_header = rearranged_header
ParametersRow = namedtuple_with_defaults('TableRow', csv_header)
writer = csv.writer(si, quoting=csv.QUOTE_ALL)
writer.writerow(csv_header)
for historyDetails in history_dicts:
parameters = load_json(historyDetails, "parameters")
historyDetails.update(parameters)
for key in unused_fields:
if key in history_dicts:
historyDetails.pop(key)
for key in [
"Plastic volume", "Plastic weight", "Filament length"
]:
if historyDetails.get(key, None):
historyDetails[key] = re.search(
r"[\d\.]*", historyDetails[key]).group(0)
if historyDetails.get("Build time", None):
# Randomly failed
try:
match = re.match(
r"(\d+) hours (\d+) minutes",
historyDetails.get("Build time", None))
historyDetails["Build time"] = (
int(match.group(1)) * 60 +
int(match.group(2))) * 60
except:
pass
parameters_row = ParametersRow(**prepare_dict(historyDetails))
writer.writerow([
getattr(parameters_row, field)
for field in parameters_row._fields
])
response = flask.make_response(si.getvalue())
response.headers["Content-type"] = "text/csv"
response.headers[
"Content-Disposition"] = "attachment; filename=octoprint_print_history(extra)_export.csv"
elif exportType == 'excel':
import xlsxwriter
workbook = xlsxwriter.Workbook(si)
worksheet = workbook.add_worksheet()
for column, header in enumerate(headers):
worksheet.write(0, column, header)
for row, historyDetails in enumerate(history_dicts):
for column, field in enumerate(fields):
value = historyDetails.get(field, '-')
worksheet.write(row + 1, column,
(value if value is not None else '-'))
workbook.close()
response = flask.make_response(si.getvalue())
response.headers["Content-type"] = "application/vnd.ms-excel"
response.headers[
"Content-Disposition"] = "attachment; filename=octoprint_print_history_export.xlsx"
return response
else:
return flask.make_response("No history file", 400)
import argparse
import collections
import torch
import numpy as np
import data_loaders as module_data
import trainer as trainers_module
import model.loss as module_loss
import model.metric as module_metric
import model as module_arch
from parse_config import ConfigParser
from trainer import Trainer, ProbabilisticTrainer
from polyaxon_client.tracking import Experiment, get_data_paths, get_outputs_path
import utils as util
CustomArgs = util.namedtuple_with_defaults('CustomArgs',
'flags type target action help',
(None, ) * 5)
class BaseRunner:
def __init__(self, description="Base Parser Description"):
self.static_arguments = argparse.ArgumentParser(
description=description)
self.dynamic_arguments = []
def add_static_arguments(self):
"""
Arguments which are not related to the json file
Where spicifc logic need to be performed for configuration
purposes for example
"""
from utils import namedtuple_with_defaults
from django.conf import settings
Junction = namedtuple_with_defaults(
'Junction', ['ref', 'ref_count', 'contig', 'contig_count', 'annotation'],
[None, -1, None, -1, []])
def get_jbrowse_link_for_contig_aligned_to_ref_genome(contig, loc):
"""Returns JBrowse to the contig aligned against the reference genome.
"""
sample_alignment = contig.experiment_sample_to_alignment
# HACK: Workaround to using type=Dataset.TYPE.BWA_ALIGN, because this
# file is imported by main/models.py, so importing Dataset here causes a
# circular import error.
# TODO(gleb): Figure out better organization
bam_dataset = sample_alignment.dataset_set.get(type='BWA BAM')
sample_bam_track = '_'.join([
bam_dataset.internal_string(sample_alignment.experiment_sample),
str(sample_alignment.alignment_group.uid)
])
track_labels = settings.JBROWSE_DEFAULT_TRACKS + [sample_bam_track]
return (contig.parent_reference_genome.get_client_jbrowse_link() +
'&loc=' + str(loc) + '&tracks=' + ','.join(track_labels))
def decorate_with_link_to_loc(contig, loc, text):
import os
from utils import DotDict, namedtuple_with_defaults, zip_namedtuple, config_as_dict
RandCropper = namedtuple_with_defaults('RandCropper',
'min_crop_scales, max_crop_scales, \
min_crop_aspect_ratios, max_crop_aspect_ratios, \
min_crop_overlaps, max_crop_overlaps, \
min_crop_sample_coverages, max_crop_sample_coverages, \
min_crop_object_coverages, max_crop_object_coverages, \
max_crop_trials',
[0.0, 1.0,
0.5, 2.0,
0.0, 1.0,
0.0, 1.0,
0.0, 1.0,
25])
RandPadder = namedtuple_with_defaults('RandPadder',
'rand_pad_prob, max_pad_scale, fill_value',
[0.0, 1.0, 127])
ColorJitter = namedtuple_with_defaults('ColorJitter',
'random_hue_prob, max_random_hue, \
random_saturation_prob, max_random_saturation, \
random_illumination_prob, max_random_illumination, \
random_contrast_prob, max_random_contrast',
[0.0, 18,
0.0, 32,
0.0, 32,
0.0, 0.5])
from utils import namedtuple_with_defaults
from django.conf import settings
Junction = namedtuple_with_defaults('Junction',
['ref', 'ref_count', 'contig', 'contig_count', 'annotation'],
[None, -1, None, -1, []])
def get_jbrowse_link_for_contig_aligned_to_ref_genome(contig, loc):
"""Returns JBrowse to the contig aligned against the reference genome.
"""
sample_alignment = contig.experiment_sample_to_alignment
# HACK: Workaround to using type=Dataset.TYPE.BWA_ALIGN, because this
# file is imported by main/models.py, so importing Dataset here causes a
# circular import error.
# TODO(gleb): Figure out better organization
bam_dataset = sample_alignment.dataset_set.get(
type='BWA BAM')
sample_bam_track = '_'.join([
bam_dataset.internal_string(sample_alignment.experiment_sample),
str(sample_alignment.alignment_group.uid)])
track_labels = settings.JBROWSE_DEFAULT_TRACKS + [sample_bam_track]
return (contig.parent_reference_genome.get_client_jbrowse_link() +
'&loc=' + str(loc) +
'&tracks=' + ','.join(track_labels))
