def find_latest_local(self):
"""
>>> self = Deployer()
>>> self.find_pretrained()
>>> self.find_latest_local()
"""
from os.path import getctime
task_clf_candidates = self.find_pretrained()
task_clf_fpaths = {}
for task_key, fpaths in task_clf_candidates.items():
# Find the classifier most recently created
fpath = fpaths[ut.argmax(map(getctime, fpaths))]
task_clf_fpaths[task_key] = fpath
return task_clf_fpaths
def find_latest_remote(self):
"""
Used to update the published dict
CommandLine:
python -m wbia.algo.verif.vsone find_latest_remote
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.verif.vsone import * # NOQA
>>> self = Deployer()
>>> task_clf_names = self.find_latest_remote()
"""
base_url = 'https://{remote}/public/models/pairclf'.format(
**self.publish_info)
import requests
import bs4
resp = requests.get(base_url)
soup = bs4.BeautifulSoup(resp.text, 'html.parser')
table = soup.findAll('table')[0]
def parse_bs_table(table):
n_columns = 0
n_rows = 0
column_names = []
# Find number of rows and columns
# we also find the column titles if we can
for row in table.find_all('tr'):
td_tags = row.find_all('td')
if len(td_tags) > 0:
n_rows += 1
if n_columns == 0:
n_columns = len(td_tags)
# Handle column names if we find them
th_tags = row.find_all('th')
if len(th_tags) > 0 and len(column_names) == 0:
for th in th_tags:
column_names.append(th.get_text())
# Safeguard on Column Titles
if len(column_names) > 0 and len(column_names) != n_columns:
raise Exception(
'Column titles do not match the number of columns')
columns = column_names if len(column_names) > 0 else range(
0, n_columns)
import pandas as pd
df = pd.DataFrame(columns=columns, index=list(range(0, n_rows)))
row_marker = 0
for row in table.find_all('tr'):
column_marker = 0
columns = row.find_all('td')
for column in columns:
df.iat[row_marker,
column_marker] = column.get_text().strip()
column_marker += 1
if len(columns) > 0:
row_marker += 1
return df
df = parse_bs_table(table)
# Find all available models
df = df[df['Name'].map(lambda x: x.endswith('.cPkl'))]
# df = df[df['Last modified'].map(len) > 0]
fname_fmt = self.fname_fmtstr + '.cPkl'
task_clf_candidates = ut.ddict(list)
import parse
for idx, row in df.iterrows():
fname = basename(row['Name'])
result = parse.parse(fname_fmt, fname)
if result:
task_key = result.named['task_key']
species = result.named['species']
task_clf_candidates[(species, task_key)].append(idx)
task_clf_fnames = ut.ddict(dict)
for key, idxs in task_clf_candidates.items():
species, task_key = key
# Find the classifier most recently created
max_idx = ut.argmax(df.loc[idxs]['Last modified'].tolist())
fname = df.loc[idxs[max_idx]]['Name']
task_clf_fnames[species][task_key] = fname
logger.info('published = ' + ut.repr2(task_clf_fnames, nl=2))
return task_clf_fnames
def update_visual_attrs(infr,
graph=None,
show_reviewed_edges=True,
show_unreviewed_edges=False,
show_inferred_diff=True,
show_inferred_same=True,
show_recent_review=False,
highlight_reviews=True,
show_inconsistency=True,
wavy=False,
simple_labels=False,
show_labels=True,
reposition=True,
use_image=False,
edge_overrides=None,
node_overrides=None,
colorby='name_label',
**kwargs
# hide_unreviewed_inferred=True
):
import wbia.plottool as pt
infr.print('update_visual_attrs', 3)
if graph is None:
graph = infr.graph
# if hide_cuts is not None:
# # show_unreviewed_cuts = not hide_cuts
# show_reviewed_cuts = not hide_cuts
if not getattr(infr, '_viz_init_nodes', False):
infr._viz_init_nodes = True
nx.set_node_attributes(graph, name='shape', values='circle')
# infr.set_node_attrs('shape', 'circle')
if getattr(infr, '_viz_image_config_dirty', True):
infr.update_node_image_attribute(graph=graph, use_image=use_image)
def get_any(dict_, keys, default=None):
for key in keys:
if key in dict_:
return dict_[key]
return default
show_cand = get_any(
kwargs, ['show_candidate_edges', 'show_candidates', 'show_cand'])
if show_cand is not None:
show_cand = True
show_reviewed_edges = True
show_unreviewed_edges = True
show_inferred_diff = True
show_inferred_same = True
if kwargs.get('show_all'):
show_cand = True
# alpha_low = .5
alpha_med = 0.9
alpha_high = 1.0
dark_background = graph.graph.get('dark_background', None)
# Ensure we are starting from a clean slate
# if reposition:
ut.nx_delete_edge_attr(graph, infr.visual_edge_attrs_appearance)
# Set annotation node labels
node_to_nid = None
if not show_labels:
nx.set_node_attributes(graph,
name='label',
values=ut.dzip(graph.nodes(), ['']))
else:
if simple_labels:
nx.set_node_attributes(
graph,
name='label',
values={n: str(n)
for n in graph.nodes()})
else:
if node_to_nid is None:
node_to_nid = nx.get_node_attributes(graph, 'name_label')
node_to_view = nx.get_node_attributes(graph, 'viewpoint')
if node_to_view:
annotnode_to_label = {
aid: 'aid=%r%s\nnid=%r' %
(aid, node_to_view[aid], node_to_nid[aid])
for aid in graph.nodes()
}
else:
annotnode_to_label = {
aid: 'aid=%r\nnid=%r' % (aid, node_to_nid[aid])
for aid in graph.nodes()
}
nx.set_node_attributes(graph,
name='label',
values=annotnode_to_label)
# NODE_COLOR: based on name_label
ut.color_nodes(graph,
labelattr=colorby,
outof=kwargs.get('outof', None),
sat_adjust=-0.4)
# EDGES:
# Grab different types of edges
edges, edge_colors = infr.get_colored_edge_weights(
graph, highlight_reviews)
# reviewed_states = nx.get_edge_attributes(graph, 'evidence_decision')
reviewed_states = {
e: infr.edge_decision(e)
for e in infr.graph.edges()
}
edge_to_inferred_state = nx.get_edge_attributes(
graph, 'inferred_state')
# dummy_edges = [edge for edge, flag in
# nx.get_edge_attributes(graph, '_dummy_edge').items()
# if flag]
edge_to_reviewid = nx.get_edge_attributes(graph, 'review_id')
recheck_edges = [
edge for edge, split in nx.get_edge_attributes(
graph, 'maybe_error').items() if split
]
decision_to_edge = ut.group_pairs(reviewed_states.items())
neg_edges = decision_to_edge[NEGTV]
pos_edges = decision_to_edge[POSTV]
incomp_edges = decision_to_edge[INCMP]
unreviewed_edges = decision_to_edge[UNREV]
inferred_same = [
edge for edge, state in edge_to_inferred_state.items()
if state == 'same'
]
inferred_diff = [
edge for edge, state in edge_to_inferred_state.items()
if state == 'diff'
]
inconsistent_external = [
edge for edge, state in edge_to_inferred_state.items()
if state == 'inconsistent_external'
]
inferred_notcomp = [
edge for edge, state in edge_to_inferred_state.items()
if state == 'notcomp'
]
reviewed_edges = incomp_edges + pos_edges + neg_edges
compared_edges = pos_edges + neg_edges
uncompared_edges = ut.setdiff(edges, compared_edges)
nontrivial_inferred_same = ut.setdiff(
inferred_same, pos_edges + neg_edges + incomp_edges)
nontrivial_inferred_diff = ut.setdiff(
inferred_diff, pos_edges + neg_edges + incomp_edges)
nontrivial_inferred_edges = nontrivial_inferred_same + nontrivial_inferred_diff
# EDGE_COLOR: based on edge_weight
nx.set_edge_attributes(graph,
name='color',
values=ut.dzip(edges, edge_colors))
# LINE_WIDTH: based on review_state
# unreviewed_width = 2.0
# reviewed_width = 5.0
unreviewed_width = 1.0
reviewed_width = 2.0
if highlight_reviews:
nx.set_edge_attributes(
graph,
name='linewidth',
values=ut.dzip(reviewed_edges, [reviewed_width]),
)
nx.set_edge_attributes(
graph,
name='linewidth',
values=ut.dzip(unreviewed_edges, [unreviewed_width]),
)
else:
nx.set_edge_attributes(graph,
name='linewidth',
values=ut.dzip(edges, [unreviewed_width]))
# EDGE_STROKE: based on decision and maybe_error
# fg = pt.WHITE if dark_background else pt.BLACK
# nx.set_edge_attributes(graph, name='stroke', values=ut.dzip(reviewed_edges, [{'linewidth': 3, 'foreground': fg}]))
if show_inconsistency:
nx.set_edge_attributes(
graph,
name='stroke',
values=ut.dzip(recheck_edges, [{
'linewidth': 5,
'foreground': infr._error_color
}]),
)
# Set linestyles to emphasize PCCs
# Dash lines between PCCs inferred to be different
nx.set_edge_attributes(graph,
name='linestyle',
values=ut.dzip(inferred_diff, ['dashed']))
# Treat incomparable/incon-external inference as different
nx.set_edge_attributes(graph,
name='linestyle',
values=ut.dzip(inferred_notcomp, ['dashed']))
nx.set_edge_attributes(graph,
name='linestyle',
values=ut.dzip(inconsistent_external,
['dashed']))
# Dot lines that we are unsure of
nx.set_edge_attributes(graph,
name='linestyle',
values=ut.dzip(unreviewed_edges, ['dotted']))
# Cut edges are implicit and dashed
# nx.set_edge_attributes(graph, name='implicit', values=ut.dzip(cut_edges, [True]))
# nx.set_edge_attributes(graph, name='linestyle', values=ut.dzip(cut_edges, ['dashed']))
# nx.set_edge_attributes(graph, name='alpha', values=ut.dzip(cut_edges, [alpha_med]))
nx.set_edge_attributes(graph,
name='implicit',
values=ut.dzip(uncompared_edges, [True]))
# Only matching edges should impose constraints on the graph layout
nx.set_edge_attributes(graph,
name='implicit',
values=ut.dzip(neg_edges, [True]))
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(neg_edges, [alpha_med]))
nx.set_edge_attributes(graph,
name='implicit',
values=ut.dzip(incomp_edges, [True]))
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(incomp_edges, [alpha_med]))
# Ensure reviewed edges are visible
nx.set_edge_attributes(graph,
name='implicit',
values=ut.dzip(reviewed_edges, [False]))
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(reviewed_edges, [alpha_high]))
if True:
# Infered same edges can be allowed to constrain in order
# to make things look nice sometimes
nx.set_edge_attributes(graph,
name='implicit',
values=ut.dzip(inferred_same, [False]))
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(inferred_same, [alpha_high]))
if not kwargs.get('show_same', True):
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(inferred_same, [0]))
if not kwargs.get('show_diff', True):
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(inferred_diff, [0]))
if not kwargs.get('show_positive_edges', True):
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(pos_edges, [0]))
if not kwargs.get('show_negative_edges', True):
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(neg_edges, [0]))
if not kwargs.get('show_incomparable_edges', True):
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(incomp_edges, [0]))
if not kwargs.get('show_between', True):
if node_to_nid is None:
node_to_nid = nx.get_node_attributes(graph, 'name_label')
between_edges = [(u, v) for u, v in edges
if node_to_nid[u] != node_to_nid[v]]
nx.set_edge_attributes(graph,
name='alpha',
values=ut.dzip(between_edges, [0]))
# SKETCH: based on inferred_edges
# Make inferred edges wavy
if wavy:
# dict(scale=3.0, length=18.0, randomness=None)]
nx.set_edge_attributes(
graph,
name='sketch',
values=ut.dzip(
nontrivial_inferred_edges,
[dict(scale=10.0, length=64.0, randomness=None)],
),
)
# Make dummy edges more transparent
# nx.set_edge_attributes(graph, name='alpha', values=ut.dzip(dummy_edges, [alpha_low]))
selected_edges = kwargs.pop('selected_edges', None)
# SHADOW: based on most recent
# Increase visibility of nodes with the most recently changed timestamp
if show_recent_review and edge_to_reviewid and selected_edges is None:
review_ids = list(edge_to_reviewid.values())
recent_idxs = ut.argmax(review_ids, multi=True)
recent_edges = ut.take(list(edge_to_reviewid.keys()), recent_idxs)
selected_edges = recent_edges
if selected_edges is not None:
# TODO: add photoshop-like parameters like
# spread and size. offset is the same as angle and distance.
nx.set_edge_attributes(
graph,
name='shadow',
values=ut.dzip(
selected_edges,
[{
'rho': 0.3,
'alpha': 0.6,
'shadow_color': 'w' if dark_background else 'k',
'offset': (0, 0),
'scale': 3.0,
}],
),
)
# Z_ORDER: make sure nodes are on top
nodes = list(graph.nodes())
nx.set_node_attributes(graph,
name='zorder',
values=ut.dzip(nodes, [10]))
nx.set_edge_attributes(graph,
name='zorder',
values=ut.dzip(edges, [0]))
nx.set_edge_attributes(graph,
name='picker',
values=ut.dzip(edges, [10]))
# VISIBILITY: Set visibility of edges based on arguments
if not show_reviewed_edges:
infr.print('Making reviewed edges invisible', 10)
nx.set_edge_attributes(graph,
name='style',
values=ut.dzip(reviewed_edges, ['invis']))
if not show_unreviewed_edges:
infr.print('Making un-reviewed edges invisible', 10)
nx.set_edge_attributes(graph,
name='style',
values=ut.dzip(unreviewed_edges, ['invis']))
if not show_inferred_same:
infr.print('Making nontrivial_same edges invisible', 10)
nx.set_edge_attributes(graph,
name='style',
values=ut.dzip(nontrivial_inferred_same,
['invis']))
if not show_inferred_diff:
infr.print('Making nontrivial_diff edges invisible', 10)
nx.set_edge_attributes(graph,
name='style',
values=ut.dzip(nontrivial_inferred_diff,
['invis']))
if selected_edges is not None:
# Always show the most recent review (remove setting of invis)
# infr.print('recent_edges = %r' % (recent_edges,))
nx.set_edge_attributes(graph,
name='style',
values=ut.dzip(selected_edges, ['']))
if reposition:
# LAYOUT: update the positioning layout
def get_layoutkw(key, default):
return kwargs.get(key, graph.graph.get(key, default))
layoutkw = dict(
prog='neato',
splines=get_layoutkw('splines', 'line'),
fontsize=get_layoutkw('fontsize', None),
fontname=get_layoutkw('fontname', None),
sep=10 / 72,
esep=1 / 72,
nodesep=0.1,
)
layoutkw.update(kwargs)
# logger.info(ut.repr3(graph.edges))
pt.nx_agraph_layout(graph, inplace=True, **layoutkw)
if edge_overrides:
for key, edge_to_attr in edge_overrides.items():
nx.set_edge_attributes(graph, name=key, values=edge_to_attr)
if node_overrides:
for key, node_to_attr in node_overrides.items():
nx.set_node_attributes(graph, name=key, values=node_to_attr)
def try_auto_review(infr, edge):
review = {
'user_id': 'algo:auto_clf',
'confidence': const.CONFIDENCE.CODE.PRETTY_SURE,
'evidence_decision': None,
'meta_decision': NULL,
'timestamp_s1': None,
'timestamp_c1': None,
'timestamp_c2': None,
'tags': [],
}
if infr.is_recovering():
# Do not autoreview if we are in an inconsistent state
infr.print('Must manually review inconsistent edge', 3)
return None
# Determine if anything passes the match threshold
primary_task = 'match_state'
try:
decision_probs = infr.task_probs[primary_task][edge]
except KeyError:
if infr.verifiers is None:
return None
if infr.verifiers.get(primary_task, None) is None:
return None
# Compute probs if they haven't been done yet
infr.ensure_priority_scores([edge])
try:
decision_probs = infr.task_probs[primary_task][edge]
except KeyError:
return None
primary_thresh = infr.task_thresh[primary_task]
decision_flags = {
k: decision_probs[k] > thresh for k, thresh in primary_thresh.items()
}
hasone = sum(decision_flags.values()) == 1
auto_flag = False
if hasone:
try:
# Check to see if it might be confounded by a photobomb
pb_probs = infr.task_probs['photobomb_state'][edge]
# pb_probs = infr.task_probs['photobomb_state'].loc[edge]
# pb_probs = data['task_probs']['photobomb_state']
pb_thresh = infr.task_thresh['photobomb_state']['pb']
confounded = pb_probs['pb'] > pb_thresh
except KeyError:
logger.info('Warning: confounding task probs not set (i.e. photobombs)')
confounded = False
if not confounded:
# decision = decision_flags.argmax()
evidence_decision = ut.argmax(decision_probs)
review['evidence_decision'] = evidence_decision
truth = infr.match_state_gt(edge)
if review['evidence_decision'] != truth:
infr.print(
'AUTOMATIC ERROR edge={}, truth={}, decision={}, probs={}'.format(
edge, truth, review['evidence_decision'], decision_probs
),
2,
color='red',
)
auto_flag = True
if auto_flag and infr.verbose > 1:
infr.print('Automatic review success')
if auto_flag:
return review
else:
return None