partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | DjangoStorageAdapter.create_many | Creates multiple statement entries. | chatterbot/storage/django_storage.py | def create_many(self, statements):
"""
Creates multiple statement entries.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
tag_cache = {}
for statement in statements:
statement_data = statement.serialize()
tag_data = statement_data.pop('tags', [])
statement_model_object = Statement(**statement_data)
if not statement.search_text:
statement_model_object.search_text = self.tagger.get_bigram_pair_string(statement.text)
if not statement.search_in_response_to and statement.in_response_to:
statement_model_object.search_in_response_to = self.tagger.get_bigram_pair_string(statement.in_response_to)
statement_model_object.save()
tags_to_add = []
for tag_name in tag_data:
if tag_name in tag_cache:
tag = tag_cache[tag_name]
else:
tag, _ = Tag.objects.get_or_create(name=tag_name)
tag_cache[tag_name] = tag
tags_to_add.append(tag)
statement_model_object.tags.add(*tags_to_add) | def create_many(self, statements):
"""
Creates multiple statement entries.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
tag_cache = {}
for statement in statements:
statement_data = statement.serialize()
tag_data = statement_data.pop('tags', [])
statement_model_object = Statement(**statement_data)
if not statement.search_text:
statement_model_object.search_text = self.tagger.get_bigram_pair_string(statement.text)
if not statement.search_in_response_to and statement.in_response_to:
statement_model_object.search_in_response_to = self.tagger.get_bigram_pair_string(statement.in_response_to)
statement_model_object.save()
tags_to_add = []
for tag_name in tag_data:
if tag_name in tag_cache:
tag = tag_cache[tag_name]
else:
tag, _ = Tag.objects.get_or_create(name=tag_name)
tag_cache[tag_name] = tag
tags_to_add.append(tag)
statement_model_object.tags.add(*tags_to_add) | [
"Creates",
"multiple",
"statement",
"entries",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L123-L157 | [
"def",
"create_many",
"(",
"self",
",",
"statements",
")",
":",
"Statement",
"=",
"self",
".",
"get_model",
"(",
"'statement'",
")",
"Tag",
"=",
"self",
".",
"get_model",
"(",
"'tag'",
")",
"tag_cache",
"=",
"{",
"}",
"for",
"statement",
"in",
"statement... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | DjangoStorageAdapter.update | Update the provided statement. | chatterbot/storage/django_storage.py | def update(self, statement):
"""
Update the provided statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
if hasattr(statement, 'id'):
statement.save()
else:
statement = Statement.objects.create(
text=statement.text,
search_text=self.tagger.get_bigram_pair_string(statement.text),
conversation=statement.conversation,
in_response_to=statement.in_response_to,
search_in_response_to=self.tagger.get_bigram_pair_string(statement.in_response_to),
created_at=statement.created_at
)
for _tag in statement.tags.all():
tag, _ = Tag.objects.get_or_create(name=_tag)
statement.tags.add(tag)
return statement | def update(self, statement):
"""
Update the provided statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
if hasattr(statement, 'id'):
statement.save()
else:
statement = Statement.objects.create(
text=statement.text,
search_text=self.tagger.get_bigram_pair_string(statement.text),
conversation=statement.conversation,
in_response_to=statement.in_response_to,
search_in_response_to=self.tagger.get_bigram_pair_string(statement.in_response_to),
created_at=statement.created_at
)
for _tag in statement.tags.all():
tag, _ = Tag.objects.get_or_create(name=_tag)
statement.tags.add(tag)
return statement | [
"Update",
"the",
"provided",
"statement",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L159-L183 | [
"def",
"update",
"(",
"self",
",",
"statement",
")",
":",
"Statement",
"=",
"self",
".",
"get_model",
"(",
"'statement'",
")",
"Tag",
"=",
"self",
".",
"get_model",
"(",
"'tag'",
")",
"if",
"hasattr",
"(",
"statement",
",",
"'id'",
")",
":",
"statement... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | DjangoStorageAdapter.get_random | Returns a random statement from the database | chatterbot/storage/django_storage.py | def get_random(self):
"""
Returns a random statement from the database
"""
Statement = self.get_model('statement')
statement = Statement.objects.order_by('?').first()
if statement is None:
raise self.EmptyDatabaseException()
return statement | def get_random(self):
"""
Returns a random statement from the database
"""
Statement = self.get_model('statement')
statement = Statement.objects.order_by('?').first()
if statement is None:
raise self.EmptyDatabaseException()
return statement | [
"Returns",
"a",
"random",
"statement",
"from",
"the",
"database"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L185-L196 | [
"def",
"get_random",
"(",
"self",
")",
":",
"Statement",
"=",
"self",
".",
"get_model",
"(",
"'statement'",
")",
"statement",
"=",
"Statement",
".",
"objects",
".",
"order_by",
"(",
"'?'",
")",
".",
"first",
"(",
")",
"if",
"statement",
"is",
"None",
"... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | DjangoStorageAdapter.remove | Removes the statement that matches the input text.
Removes any responses from statements if the response text matches the
input text. | chatterbot/storage/django_storage.py | def remove(self, statement_text):
"""
Removes the statement that matches the input text.
Removes any responses from statements if the response text matches the
input text.
"""
Statement = self.get_model('statement')
statements = Statement.objects.filter(text=statement_text)
statements.delete() | def remove(self, statement_text):
"""
Removes the statement that matches the input text.
Removes any responses from statements if the response text matches the
input text.
"""
Statement = self.get_model('statement')
statements = Statement.objects.filter(text=statement_text)
statements.delete() | [
"Removes",
"the",
"statement",
"that",
"matches",
"the",
"input",
"text",
".",
"Removes",
"any",
"responses",
"from",
"statements",
"if",
"the",
"response",
"text",
"matches",
"the",
"input",
"text",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L198-L208 | [
"def",
"remove",
"(",
"self",
",",
"statement_text",
")",
":",
"Statement",
"=",
"self",
".",
"get_model",
"(",
"'statement'",
")",
"statements",
"=",
"Statement",
".",
"objects",
".",
"filter",
"(",
"text",
"=",
"statement_text",
")",
"statements",
".",
"... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | DjangoStorageAdapter.drop | Remove all data from the database. | chatterbot/storage/django_storage.py | def drop(self):
"""
Remove all data from the database.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
Statement.objects.all().delete()
Tag.objects.all().delete() | def drop(self):
"""
Remove all data from the database.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
Statement.objects.all().delete()
Tag.objects.all().delete() | [
"Remove",
"all",
"data",
"from",
"the",
"database",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L210-L218 | [
"def",
"drop",
"(",
"self",
")",
":",
"Statement",
"=",
"self",
".",
"get_model",
"(",
"'statement'",
")",
"Tag",
"=",
"self",
".",
"get_model",
"(",
"'tag'",
")",
"Statement",
".",
"objects",
".",
"all",
"(",
")",
".",
"delete",
"(",
")",
"Tag",
"... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | clean_whitespace | Remove any consecutive whitespace characters from the statement text. | chatterbot/preprocessors.py | def clean_whitespace(statement):
"""
Remove any consecutive whitespace characters from the statement text.
"""
import re
# Replace linebreaks and tabs with spaces
statement.text = statement.text.replace('\n', ' ').replace('\r', ' ').replace('\t', ' ')
# Remove any leeding or trailing whitespace
statement.text = statement.text.strip()
# Remove consecutive spaces
statement.text = re.sub(' +', ' ', statement.text)
return statement | def clean_whitespace(statement):
"""
Remove any consecutive whitespace characters from the statement text.
"""
import re
# Replace linebreaks and tabs with spaces
statement.text = statement.text.replace('\n', ' ').replace('\r', ' ').replace('\t', ' ')
# Remove any leeding or trailing whitespace
statement.text = statement.text.strip()
# Remove consecutive spaces
statement.text = re.sub(' +', ' ', statement.text)
return statement | [
"Remove",
"any",
"consecutive",
"whitespace",
"characters",
"from",
"the",
"statement",
"text",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/preprocessors.py#L6-L21 | [
"def",
"clean_whitespace",
"(",
"statement",
")",
":",
"import",
"re",
"# Replace linebreaks and tabs with spaces",
"statement",
".",
"text",
"=",
"statement",
".",
"text",
".",
"replace",
"(",
"'\\n'",
",",
"' '",
")",
".",
"replace",
"(",
"'\\r'",
",",
"' '"... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | unescape_html | Convert escaped html characters into unescaped html characters.
For example: "<b>" becomes "<b>". | chatterbot/preprocessors.py | def unescape_html(statement):
"""
Convert escaped html characters into unescaped html characters.
For example: "<b>" becomes "<b>".
"""
import html
statement.text = html.unescape(statement.text)
return statement | def unescape_html(statement):
"""
Convert escaped html characters into unescaped html characters.
For example: "<b>" becomes "<b>".
"""
import html
statement.text = html.unescape(statement.text)
return statement | [
"Convert",
"escaped",
"html",
"characters",
"into",
"unescaped",
"html",
"characters",
".",
"For",
"example",
":",
"<",
";",
"b>",
";",
"becomes",
"<b",
">",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/preprocessors.py#L24-L33 | [
"def",
"unescape_html",
"(",
"statement",
")",
":",
"import",
"html",
"statement",
".",
"text",
"=",
"html",
".",
"unescape",
"(",
"statement",
".",
"text",
")",
"return",
"statement"
] | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | convert_to_ascii | Converts unicode characters to ASCII character equivalents.
For example: "på fédéral" becomes "pa federal". | chatterbot/preprocessors.py | def convert_to_ascii(statement):
"""
Converts unicode characters to ASCII character equivalents.
For example: "på fédéral" becomes "pa federal".
"""
import unicodedata
text = unicodedata.normalize('NFKD', statement.text)
text = text.encode('ascii', 'ignore').decode('utf-8')
statement.text = str(text)
return statement | def convert_to_ascii(statement):
"""
Converts unicode characters to ASCII character equivalents.
For example: "på fédéral" becomes "pa federal".
"""
import unicodedata
text = unicodedata.normalize('NFKD', statement.text)
text = text.encode('ascii', 'ignore').decode('utf-8')
statement.text = str(text)
return statement | [
"Converts",
"unicode",
"characters",
"to",
"ASCII",
"character",
"equivalents",
".",
"For",
"example",
":",
"på",
"fédéral",
"becomes",
"pa",
"federal",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/preprocessors.py#L36-L47 | [
"def",
"convert_to_ascii",
"(",
"statement",
")",
":",
"import",
"unicodedata",
"text",
"=",
"unicodedata",
".",
"normalize",
"(",
"'NFKD'",
",",
"statement",
".",
"text",
")",
"text",
"=",
"text",
".",
"encode",
"(",
"'ascii'",
",",
"'ignore'",
")",
".",
... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | convert_string_to_number | Convert strings to numbers | chatterbot/parsing.py | def convert_string_to_number(value):
"""
Convert strings to numbers
"""
if value is None:
return 1
if isinstance(value, int):
return value
if value.isdigit():
return int(value)
num_list = map(lambda s: NUMBERS[s], re.findall(numbers + '+', value.lower()))
return sum(num_list) | def convert_string_to_number(value):
"""
Convert strings to numbers
"""
if value is None:
return 1
if isinstance(value, int):
return value
if value.isdigit():
return int(value)
num_list = map(lambda s: NUMBERS[s], re.findall(numbers + '+', value.lower()))
return sum(num_list) | [
"Convert",
"strings",
"to",
"numbers"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L506-L517 | [
"def",
"convert_string_to_number",
"(",
"value",
")",
":",
"if",
"value",
"is",
"None",
":",
"return",
"1",
"if",
"isinstance",
"(",
"value",
",",
"int",
")",
":",
"return",
"value",
"if",
"value",
".",
"isdigit",
"(",
")",
":",
"return",
"int",
"(",
... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | convert_time_to_hour_minute | Convert time to hour, minute | chatterbot/parsing.py | def convert_time_to_hour_minute(hour, minute, convention):
"""
Convert time to hour, minute
"""
if hour is None:
hour = 0
if minute is None:
minute = 0
if convention is None:
convention = 'am'
hour = int(hour)
minute = int(minute)
if convention.lower() == 'pm':
hour += 12
return {'hours': hour, 'minutes': minute} | def convert_time_to_hour_minute(hour, minute, convention):
"""
Convert time to hour, minute
"""
if hour is None:
hour = 0
if minute is None:
minute = 0
if convention is None:
convention = 'am'
hour = int(hour)
minute = int(minute)
if convention.lower() == 'pm':
hour += 12
return {'hours': hour, 'minutes': minute} | [
"Convert",
"time",
"to",
"hour",
"minute"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L520-L537 | [
"def",
"convert_time_to_hour_minute",
"(",
"hour",
",",
"minute",
",",
"convention",
")",
":",
"if",
"hour",
"is",
"None",
":",
"hour",
"=",
"0",
"if",
"minute",
"is",
"None",
":",
"minute",
"=",
"0",
"if",
"convention",
"is",
"None",
":",
"convention",
... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | date_from_quarter | Extract date from quarter of a year | chatterbot/parsing.py | def date_from_quarter(base_date, ordinal, year):
"""
Extract date from quarter of a year
"""
interval = 3
month_start = interval * (ordinal - 1)
if month_start < 0:
month_start = 9
month_end = month_start + interval
if month_start == 0:
month_start = 1
return [
datetime(year, month_start, 1),
datetime(year, month_end, calendar.monthrange(year, month_end)[1])
] | def date_from_quarter(base_date, ordinal, year):
"""
Extract date from quarter of a year
"""
interval = 3
month_start = interval * (ordinal - 1)
if month_start < 0:
month_start = 9
month_end = month_start + interval
if month_start == 0:
month_start = 1
return [
datetime(year, month_start, 1),
datetime(year, month_end, calendar.monthrange(year, month_end)[1])
] | [
"Extract",
"date",
"from",
"quarter",
"of",
"a",
"year"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L540-L554 | [
"def",
"date_from_quarter",
"(",
"base_date",
",",
"ordinal",
",",
"year",
")",
":",
"interval",
"=",
"3",
"month_start",
"=",
"interval",
"*",
"(",
"ordinal",
"-",
"1",
")",
"if",
"month_start",
"<",
"0",
":",
"month_start",
"=",
"9",
"month_end",
"=",
... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | date_from_relative_day | Converts relative day to time
Ex: this tuesday, last tuesday | chatterbot/parsing.py | def date_from_relative_day(base_date, time, dow):
"""
Converts relative day to time
Ex: this tuesday, last tuesday
"""
# Reset date to start of the day
base_date = datetime(base_date.year, base_date.month, base_date.day)
time = time.lower()
dow = dow.lower()
if time == 'this' or time == 'coming':
# Else day of week
num = HASHWEEKDAYS[dow]
return this_week_day(base_date, num)
elif time == 'last' or time == 'previous':
# Else day of week
num = HASHWEEKDAYS[dow]
return previous_week_day(base_date, num)
elif time == 'next' or time == 'following':
# Else day of week
num = HASHWEEKDAYS[dow]
return next_week_day(base_date, num) | def date_from_relative_day(base_date, time, dow):
"""
Converts relative day to time
Ex: this tuesday, last tuesday
"""
# Reset date to start of the day
base_date = datetime(base_date.year, base_date.month, base_date.day)
time = time.lower()
dow = dow.lower()
if time == 'this' or time == 'coming':
# Else day of week
num = HASHWEEKDAYS[dow]
return this_week_day(base_date, num)
elif time == 'last' or time == 'previous':
# Else day of week
num = HASHWEEKDAYS[dow]
return previous_week_day(base_date, num)
elif time == 'next' or time == 'following':
# Else day of week
num = HASHWEEKDAYS[dow]
return next_week_day(base_date, num) | [
"Converts",
"relative",
"day",
"to",
"time",
"Ex",
":",
"this",
"tuesday",
"last",
"tuesday"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L557-L577 | [
"def",
"date_from_relative_day",
"(",
"base_date",
",",
"time",
",",
"dow",
")",
":",
"# Reset date to start of the day",
"base_date",
"=",
"datetime",
"(",
"base_date",
".",
"year",
",",
"base_date",
".",
"month",
",",
"base_date",
".",
"day",
")",
"time",
"=... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | date_from_relative_week_year | Converts relative day to time
Eg. this tuesday, last tuesday | chatterbot/parsing.py | def date_from_relative_week_year(base_date, time, dow, ordinal=1):
"""
Converts relative day to time
Eg. this tuesday, last tuesday
"""
# If there is an ordinal (next 3 weeks) => return a start and end range
# Reset date to start of the day
relative_date = datetime(base_date.year, base_date.month, base_date.day)
ord = convert_string_to_number(ordinal)
if dow in year_variations:
if time == 'this' or time == 'coming':
return datetime(relative_date.year, 1, 1)
elif time == 'last' or time == 'previous':
return datetime(relative_date.year - 1, relative_date.month, 1)
elif time == 'next' or time == 'following':
return relative_date + timedelta(ord * 365)
elif time == 'end of the':
return datetime(relative_date.year, 12, 31)
elif dow in month_variations:
if time == 'this':
return datetime(relative_date.year, relative_date.month, relative_date.day)
elif time == 'last' or time == 'previous':
return datetime(relative_date.year, relative_date.month - 1, relative_date.day)
elif time == 'next' or time == 'following':
if relative_date.month + ord >= 12:
month = relative_date.month - 1 + ord
year = relative_date.year + month // 12
month = month % 12 + 1
day = min(relative_date.day, calendar.monthrange(year, month)[1])
return datetime(year, month, day)
else:
return datetime(relative_date.year, relative_date.month + ord, relative_date.day)
elif time == 'end of the':
return datetime(
relative_date.year,
relative_date.month,
calendar.monthrange(relative_date.year, relative_date.month)[1]
)
elif dow in week_variations:
if time == 'this':
return relative_date - timedelta(days=relative_date.weekday())
elif time == 'last' or time == 'previous':
return relative_date - timedelta(weeks=1)
elif time == 'next' or time == 'following':
return relative_date + timedelta(weeks=ord)
elif time == 'end of the':
day_of_week = base_date.weekday()
return day_of_week + timedelta(days=6 - relative_date.weekday())
elif dow in day_variations:
if time == 'this':
return relative_date
elif time == 'last' or time == 'previous':
return relative_date - timedelta(days=1)
elif time == 'next' or time == 'following':
return relative_date + timedelta(days=ord)
elif time == 'end of the':
return datetime(relative_date.year, relative_date.month, relative_date.day, 23, 59, 59) | def date_from_relative_week_year(base_date, time, dow, ordinal=1):
"""
Converts relative day to time
Eg. this tuesday, last tuesday
"""
# If there is an ordinal (next 3 weeks) => return a start and end range
# Reset date to start of the day
relative_date = datetime(base_date.year, base_date.month, base_date.day)
ord = convert_string_to_number(ordinal)
if dow in year_variations:
if time == 'this' or time == 'coming':
return datetime(relative_date.year, 1, 1)
elif time == 'last' or time == 'previous':
return datetime(relative_date.year - 1, relative_date.month, 1)
elif time == 'next' or time == 'following':
return relative_date + timedelta(ord * 365)
elif time == 'end of the':
return datetime(relative_date.year, 12, 31)
elif dow in month_variations:
if time == 'this':
return datetime(relative_date.year, relative_date.month, relative_date.day)
elif time == 'last' or time == 'previous':
return datetime(relative_date.year, relative_date.month - 1, relative_date.day)
elif time == 'next' or time == 'following':
if relative_date.month + ord >= 12:
month = relative_date.month - 1 + ord
year = relative_date.year + month // 12
month = month % 12 + 1
day = min(relative_date.day, calendar.monthrange(year, month)[1])
return datetime(year, month, day)
else:
return datetime(relative_date.year, relative_date.month + ord, relative_date.day)
elif time == 'end of the':
return datetime(
relative_date.year,
relative_date.month,
calendar.monthrange(relative_date.year, relative_date.month)[1]
)
elif dow in week_variations:
if time == 'this':
return relative_date - timedelta(days=relative_date.weekday())
elif time == 'last' or time == 'previous':
return relative_date - timedelta(weeks=1)
elif time == 'next' or time == 'following':
return relative_date + timedelta(weeks=ord)
elif time == 'end of the':
day_of_week = base_date.weekday()
return day_of_week + timedelta(days=6 - relative_date.weekday())
elif dow in day_variations:
if time == 'this':
return relative_date
elif time == 'last' or time == 'previous':
return relative_date - timedelta(days=1)
elif time == 'next' or time == 'following':
return relative_date + timedelta(days=ord)
elif time == 'end of the':
return datetime(relative_date.year, relative_date.month, relative_date.day, 23, 59, 59) | [
"Converts",
"relative",
"day",
"to",
"time",
"Eg",
".",
"this",
"tuesday",
"last",
"tuesday"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L580-L636 | [
"def",
"date_from_relative_week_year",
"(",
"base_date",
",",
"time",
",",
"dow",
",",
"ordinal",
"=",
"1",
")",
":",
"# If there is an ordinal (next 3 weeks) => return a start and end range",
"# Reset date to start of the day",
"relative_date",
"=",
"datetime",
"(",
"base_da... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | date_from_adverb | Convert Day adverbs to dates
Tomorrow => Date
Today => Date | chatterbot/parsing.py | def date_from_adverb(base_date, name):
"""
Convert Day adverbs to dates
Tomorrow => Date
Today => Date
"""
# Reset date to start of the day
adverb_date = datetime(base_date.year, base_date.month, base_date.day)
if name == 'today' or name == 'tonite' or name == 'tonight':
return adverb_date.today()
elif name == 'yesterday':
return adverb_date - timedelta(days=1)
elif name == 'tomorrow' or name == 'tom':
return adverb_date + timedelta(days=1) | def date_from_adverb(base_date, name):
"""
Convert Day adverbs to dates
Tomorrow => Date
Today => Date
"""
# Reset date to start of the day
adverb_date = datetime(base_date.year, base_date.month, base_date.day)
if name == 'today' or name == 'tonite' or name == 'tonight':
return adverb_date.today()
elif name == 'yesterday':
return adverb_date - timedelta(days=1)
elif name == 'tomorrow' or name == 'tom':
return adverb_date + timedelta(days=1) | [
"Convert",
"Day",
"adverbs",
"to",
"dates",
"Tomorrow",
"=",
">",
"Date",
"Today",
"=",
">",
"Date"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L639-L652 | [
"def",
"date_from_adverb",
"(",
"base_date",
",",
"name",
")",
":",
"# Reset date to start of the day",
"adverb_date",
"=",
"datetime",
"(",
"base_date",
".",
"year",
",",
"base_date",
".",
"month",
",",
"base_date",
".",
"day",
")",
"if",
"name",
"==",
"'toda... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | date_from_duration | Find dates from duration
Eg: 20 days from now
Currently does not support strings like "20 days from last monday". | chatterbot/parsing.py | def date_from_duration(base_date, number_as_string, unit, duration, base_time=None):
"""
Find dates from duration
Eg: 20 days from now
Currently does not support strings like "20 days from last monday".
"""
# Check if query is `2 days before yesterday` or `day before yesterday`
if base_time is not None:
base_date = date_from_adverb(base_date, base_time)
num = convert_string_to_number(number_as_string)
if unit in day_variations:
args = {'days': num}
elif unit in minute_variations:
args = {'minutes': num}
elif unit in week_variations:
args = {'weeks': num}
elif unit in month_variations:
args = {'days': 365 * num / 12}
elif unit in year_variations:
args = {'years': num}
if duration == 'ago' or duration == 'before' or duration == 'earlier':
if 'years' in args:
return datetime(base_date.year - args['years'], base_date.month, base_date.day)
return base_date - timedelta(**args)
elif duration == 'after' or duration == 'later' or duration == 'from now':
if 'years' in args:
return datetime(base_date.year + args['years'], base_date.month, base_date.day)
return base_date + timedelta(**args) | def date_from_duration(base_date, number_as_string, unit, duration, base_time=None):
"""
Find dates from duration
Eg: 20 days from now
Currently does not support strings like "20 days from last monday".
"""
# Check if query is `2 days before yesterday` or `day before yesterday`
if base_time is not None:
base_date = date_from_adverb(base_date, base_time)
num = convert_string_to_number(number_as_string)
if unit in day_variations:
args = {'days': num}
elif unit in minute_variations:
args = {'minutes': num}
elif unit in week_variations:
args = {'weeks': num}
elif unit in month_variations:
args = {'days': 365 * num / 12}
elif unit in year_variations:
args = {'years': num}
if duration == 'ago' or duration == 'before' or duration == 'earlier':
if 'years' in args:
return datetime(base_date.year - args['years'], base_date.month, base_date.day)
return base_date - timedelta(**args)
elif duration == 'after' or duration == 'later' or duration == 'from now':
if 'years' in args:
return datetime(base_date.year + args['years'], base_date.month, base_date.day)
return base_date + timedelta(**args) | [
"Find",
"dates",
"from",
"duration",
"Eg",
":",
"20",
"days",
"from",
"now",
"Currently",
"does",
"not",
"support",
"strings",
"like",
"20",
"days",
"from",
"last",
"monday",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L655-L682 | [
"def",
"date_from_duration",
"(",
"base_date",
",",
"number_as_string",
",",
"unit",
",",
"duration",
",",
"base_time",
"=",
"None",
")",
":",
"# Check if query is `2 days before yesterday` or `day before yesterday`",
"if",
"base_time",
"is",
"not",
"None",
":",
"base_d... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | this_week_day | Finds coming weekday | chatterbot/parsing.py | def this_week_day(base_date, weekday):
"""
Finds coming weekday
"""
day_of_week = base_date.weekday()
# If today is Tuesday and the query is `this monday`
# We should output the next_week monday
if day_of_week > weekday:
return next_week_day(base_date, weekday)
start_of_this_week = base_date - timedelta(days=day_of_week + 1)
day = start_of_this_week + timedelta(days=1)
while day.weekday() != weekday:
day = day + timedelta(days=1)
return day | def this_week_day(base_date, weekday):
"""
Finds coming weekday
"""
day_of_week = base_date.weekday()
# If today is Tuesday and the query is `this monday`
# We should output the next_week monday
if day_of_week > weekday:
return next_week_day(base_date, weekday)
start_of_this_week = base_date - timedelta(days=day_of_week + 1)
day = start_of_this_week + timedelta(days=1)
while day.weekday() != weekday:
day = day + timedelta(days=1)
return day | [
"Finds",
"coming",
"weekday"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L685-L698 | [
"def",
"this_week_day",
"(",
"base_date",
",",
"weekday",
")",
":",
"day_of_week",
"=",
"base_date",
".",
"weekday",
"(",
")",
"# If today is Tuesday and the query is `this monday`",
"# We should output the next_week monday",
"if",
"day_of_week",
">",
"weekday",
":",
"ret... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | previous_week_day | Finds previous weekday | chatterbot/parsing.py | def previous_week_day(base_date, weekday):
"""
Finds previous weekday
"""
day = base_date - timedelta(days=1)
while day.weekday() != weekday:
day = day - timedelta(days=1)
return day | def previous_week_day(base_date, weekday):
"""
Finds previous weekday
"""
day = base_date - timedelta(days=1)
while day.weekday() != weekday:
day = day - timedelta(days=1)
return day | [
"Finds",
"previous",
"weekday"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L701-L708 | [
"def",
"previous_week_day",
"(",
"base_date",
",",
"weekday",
")",
":",
"day",
"=",
"base_date",
"-",
"timedelta",
"(",
"days",
"=",
"1",
")",
"while",
"day",
".",
"weekday",
"(",
")",
"!=",
"weekday",
":",
"day",
"=",
"day",
"-",
"timedelta",
"(",
"... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | next_week_day | Finds next weekday | chatterbot/parsing.py | def next_week_day(base_date, weekday):
"""
Finds next weekday
"""
day_of_week = base_date.weekday()
end_of_this_week = base_date + timedelta(days=6 - day_of_week)
day = end_of_this_week + timedelta(days=1)
while day.weekday() != weekday:
day = day + timedelta(days=1)
return day | def next_week_day(base_date, weekday):
"""
Finds next weekday
"""
day_of_week = base_date.weekday()
end_of_this_week = base_date + timedelta(days=6 - day_of_week)
day = end_of_this_week + timedelta(days=1)
while day.weekday() != weekday:
day = day + timedelta(days=1)
return day | [
"Finds",
"next",
"weekday"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L711-L720 | [
"def",
"next_week_day",
"(",
"base_date",
",",
"weekday",
")",
":",
"day_of_week",
"=",
"base_date",
".",
"weekday",
"(",
")",
"end_of_this_week",
"=",
"base_date",
"+",
"timedelta",
"(",
"days",
"=",
"6",
"-",
"day_of_week",
")",
"day",
"=",
"end_of_this_we... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | datetime_parsing | Extract datetime objects from a string of text. | chatterbot/parsing.py | def datetime_parsing(text, base_date=datetime.now()):
"""
Extract datetime objects from a string of text.
"""
matches = []
found_array = []
# Find the position in the string
for expression, function in regex:
for match in expression.finditer(text):
matches.append((match.group(), function(match, base_date), match.span()))
# Wrap the matched text with TAG element to prevent nested selections
for match, value, spans in matches:
subn = re.subn(
'(?!<TAG[^>]*?>)' + match + '(?![^<]*?</TAG>)', '<TAG>' + match + '</TAG>', text
)
text = subn[0]
is_substituted = subn[1]
if is_substituted != 0:
found_array.append((match, value, spans))
# To preserve order of the match, sort based on the start position
return sorted(found_array, key=lambda match: match and match[2][0]) | def datetime_parsing(text, base_date=datetime.now()):
"""
Extract datetime objects from a string of text.
"""
matches = []
found_array = []
# Find the position in the string
for expression, function in regex:
for match in expression.finditer(text):
matches.append((match.group(), function(match, base_date), match.span()))
# Wrap the matched text with TAG element to prevent nested selections
for match, value, spans in matches:
subn = re.subn(
'(?!<TAG[^>]*?>)' + match + '(?![^<]*?</TAG>)', '<TAG>' + match + '</TAG>', text
)
text = subn[0]
is_substituted = subn[1]
if is_substituted != 0:
found_array.append((match, value, spans))
# To preserve order of the match, sort based on the start position
return sorted(found_array, key=lambda match: match and match[2][0]) | [
"Extract",
"datetime",
"objects",
"from",
"a",
"string",
"of",
"text",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/parsing.py#L723-L746 | [
"def",
"datetime_parsing",
"(",
"text",
",",
"base_date",
"=",
"datetime",
".",
"now",
"(",
")",
")",
":",
"matches",
"=",
"[",
"]",
"found_array",
"=",
"[",
"]",
"# Find the position in the string",
"for",
"expression",
",",
"function",
"in",
"regex",
":",
... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | IndexedTextSearch.search | Search for close matches to the input. Confidence scores for
subsequent results will order of increasing value.
:param input_statement: A statement.
:type input_statement: chatterbot.conversation.Statement
:param **additional_parameters: Additional parameters to be passed
to the ``filter`` method of the storage adapter when searching.
:rtype: Generator yielding one closest matching statement at a time. | chatterbot/search.py | def search(self, input_statement, **additional_parameters):
"""
Search for close matches to the input. Confidence scores for
subsequent results will order of increasing value.
:param input_statement: A statement.
:type input_statement: chatterbot.conversation.Statement
:param **additional_parameters: Additional parameters to be passed
to the ``filter`` method of the storage adapter when searching.
:rtype: Generator yielding one closest matching statement at a time.
"""
self.chatbot.logger.info('Beginning search for close text match')
input_search_text = input_statement.search_text
if not input_statement.search_text:
self.chatbot.logger.warn(
'No value for search_text was available on the provided input'
)
input_search_text = self.chatbot.storage.tagger.get_bigram_pair_string(
input_statement.text
)
search_parameters = {
'search_text_contains': input_search_text,
'persona_not_startswith': 'bot:',
'page_size': self.search_page_size
}
if additional_parameters:
search_parameters.update(additional_parameters)
statement_list = self.chatbot.storage.filter(**search_parameters)
closest_match = Statement(text='')
closest_match.confidence = 0
self.chatbot.logger.info('Processing search results')
# Find the closest matching known statement
for statement in statement_list:
confidence = self.compare_statements(input_statement, statement)
if confidence > closest_match.confidence:
statement.confidence = confidence
closest_match = statement
self.chatbot.logger.info('Similar text found: {} {}'.format(
closest_match.text, confidence
))
yield closest_match | def search(self, input_statement, **additional_parameters):
"""
Search for close matches to the input. Confidence scores for
subsequent results will order of increasing value.
:param input_statement: A statement.
:type input_statement: chatterbot.conversation.Statement
:param **additional_parameters: Additional parameters to be passed
to the ``filter`` method of the storage adapter when searching.
:rtype: Generator yielding one closest matching statement at a time.
"""
self.chatbot.logger.info('Beginning search for close text match')
input_search_text = input_statement.search_text
if not input_statement.search_text:
self.chatbot.logger.warn(
'No value for search_text was available on the provided input'
)
input_search_text = self.chatbot.storage.tagger.get_bigram_pair_string(
input_statement.text
)
search_parameters = {
'search_text_contains': input_search_text,
'persona_not_startswith': 'bot:',
'page_size': self.search_page_size
}
if additional_parameters:
search_parameters.update(additional_parameters)
statement_list = self.chatbot.storage.filter(**search_parameters)
closest_match = Statement(text='')
closest_match.confidence = 0
self.chatbot.logger.info('Processing search results')
# Find the closest matching known statement
for statement in statement_list:
confidence = self.compare_statements(input_statement, statement)
if confidence > closest_match.confidence:
statement.confidence = confidence
closest_match = statement
self.chatbot.logger.info('Similar text found: {} {}'.format(
closest_match.text, confidence
))
yield closest_match | [
"Search",
"for",
"close",
"matches",
"to",
"the",
"input",
".",
"Confidence",
"scores",
"for",
"subsequent",
"results",
"will",
"order",
"of",
"increasing",
"value",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/search.py#L35-L89 | [
"def",
"search",
"(",
"self",
",",
"input_statement",
",",
"*",
"*",
"additional_parameters",
")",
":",
"self",
".",
"chatbot",
".",
"logger",
".",
"info",
"(",
"'Beginning search for close text match'",
")",
"input_search_text",
"=",
"input_statement",
".",
"sear... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | TkinterGUIExample.initialize | Set window layout. | examples/tkinter_gui.py | def initialize(self):
"""
Set window layout.
"""
self.grid()
self.respond = ttk.Button(self, text='Get Response', command=self.get_response)
self.respond.grid(column=0, row=0, sticky='nesw', padx=3, pady=3)
self.usr_input = ttk.Entry(self, state='normal')
self.usr_input.grid(column=1, row=0, sticky='nesw', padx=3, pady=3)
self.conversation_lbl = ttk.Label(self, anchor=tk.E, text='Conversation:')
self.conversation_lbl.grid(column=0, row=1, sticky='nesw', padx=3, pady=3)
self.conversation = ScrolledText.ScrolledText(self, state='disabled')
self.conversation.grid(column=0, row=2, columnspan=2, sticky='nesw', padx=3, pady=3) | def initialize(self):
"""
Set window layout.
"""
self.grid()
self.respond = ttk.Button(self, text='Get Response', command=self.get_response)
self.respond.grid(column=0, row=0, sticky='nesw', padx=3, pady=3)
self.usr_input = ttk.Entry(self, state='normal')
self.usr_input.grid(column=1, row=0, sticky='nesw', padx=3, pady=3)
self.conversation_lbl = ttk.Label(self, anchor=tk.E, text='Conversation:')
self.conversation_lbl.grid(column=0, row=1, sticky='nesw', padx=3, pady=3)
self.conversation = ScrolledText.ScrolledText(self, state='disabled')
self.conversation.grid(column=0, row=2, columnspan=2, sticky='nesw', padx=3, pady=3) | [
"Set",
"window",
"layout",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/examples/tkinter_gui.py#L33-L49 | [
"def",
"initialize",
"(",
"self",
")",
":",
"self",
".",
"grid",
"(",
")",
"self",
".",
"respond",
"=",
"ttk",
".",
"Button",
"(",
"self",
",",
"text",
"=",
"'Get Response'",
",",
"command",
"=",
"self",
".",
"get_response",
")",
"self",
".",
"respon... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | TkinterGUIExample.get_response | Get a response from the chatbot and display it. | examples/tkinter_gui.py | def get_response(self):
"""
Get a response from the chatbot and display it.
"""
user_input = self.usr_input.get()
self.usr_input.delete(0, tk.END)
response = self.chatbot.get_response(user_input)
self.conversation['state'] = 'normal'
self.conversation.insert(
tk.END, "Human: " + user_input + "\n" + "ChatBot: " + str(response.text) + "\n"
)
self.conversation['state'] = 'disabled'
time.sleep(0.5) | def get_response(self):
"""
Get a response from the chatbot and display it.
"""
user_input = self.usr_input.get()
self.usr_input.delete(0, tk.END)
response = self.chatbot.get_response(user_input)
self.conversation['state'] = 'normal'
self.conversation.insert(
tk.END, "Human: " + user_input + "\n" + "ChatBot: " + str(response.text) + "\n"
)
self.conversation['state'] = 'disabled'
time.sleep(0.5) | [
"Get",
"a",
"response",
"from",
"the",
"chatbot",
"and",
"display",
"it",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/examples/tkinter_gui.py#L51-L66 | [
"def",
"get_response",
"(",
"self",
")",
":",
"user_input",
"=",
"self",
".",
"usr_input",
".",
"get",
"(",
")",
"self",
".",
"usr_input",
".",
"delete",
"(",
"0",
",",
"tk",
".",
"END",
")",
"response",
"=",
"self",
".",
"chatbot",
".",
"get_respons... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | AbstractBaseStatement.add_tags | Add a list of strings to the statement as tags.
(Overrides the method from StatementMixin) | chatterbot/ext/django_chatterbot/abstract_models.py | def add_tags(self, *tags):
"""
Add a list of strings to the statement as tags.
(Overrides the method from StatementMixin)
"""
for _tag in tags:
self.tags.get_or_create(name=_tag) | def add_tags(self, *tags):
"""
Add a list of strings to the statement as tags.
(Overrides the method from StatementMixin)
"""
for _tag in tags:
self.tags.get_or_create(name=_tag) | [
"Add",
"a",
"list",
"of",
"strings",
"to",
"the",
"statement",
"as",
"tags",
".",
"(",
"Overrides",
"the",
"method",
"from",
"StatementMixin",
")"
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/ext/django_chatterbot/abstract_models.py#L110-L116 | [
"def",
"add_tags",
"(",
"self",
",",
"*",
"tags",
")",
":",
"for",
"_tag",
"in",
"tags",
":",
"self",
".",
"tags",
".",
"get_or_create",
"(",
"name",
"=",
"_tag",
")"
] | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | SvelteComponent | Display svelte components in iPython.
Args:
name: name of svelte component (must match component filename when built)
path: path to compile svelte .js file or source svelte .html file.
(If html file, we try to call svelte and build the file.)
Returns:
A function mapping data to a rendered svelte component in ipython. | lucid/scratch/web/svelte.py | def SvelteComponent(name, path):
"""Display svelte components in iPython.
Args:
name: name of svelte component (must match component filename when built)
path: path to compile svelte .js file or source svelte .html file.
(If html file, we try to call svelte and build the file.)
Returns:
A function mapping data to a rendered svelte component in ipython.
"""
if path[-3:] == ".js":
js_path = path
elif path[-5:] == ".html":
print("Trying to build svelte component from html...")
js_path = build_svelte(path)
js_content = read(js_path, mode='r')
def inner(data):
id_str = js_id(name)
html = _template \
.replace("$js", js_content) \
.replace("$name", name) \
.replace("$data", json.dumps(data)) \
.replace("$id", id_str)
_display_html(html)
return inner | def SvelteComponent(name, path):
"""Display svelte components in iPython.
Args:
name: name of svelte component (must match component filename when built)
path: path to compile svelte .js file or source svelte .html file.
(If html file, we try to call svelte and build the file.)
Returns:
A function mapping data to a rendered svelte component in ipython.
"""
if path[-3:] == ".js":
js_path = path
elif path[-5:] == ".html":
print("Trying to build svelte component from html...")
js_path = build_svelte(path)
js_content = read(js_path, mode='r')
def inner(data):
id_str = js_id(name)
html = _template \
.replace("$js", js_content) \
.replace("$name", name) \
.replace("$data", json.dumps(data)) \
.replace("$id", id_str)
_display_html(html)
return inner | [
"Display",
"svelte",
"components",
"in",
"iPython",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/scratch/web/svelte.py#L43-L68 | [
"def",
"SvelteComponent",
"(",
"name",
",",
"path",
")",
":",
"if",
"path",
"[",
"-",
"3",
":",
"]",
"==",
"\".js\"",
":",
"js_path",
"=",
"path",
"elif",
"path",
"[",
"-",
"5",
":",
"]",
"==",
"\".html\"",
":",
"print",
"(",
"\"Trying to build svelt... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | save_json | Save object as json on CNS. | lucid/misc/io/saving.py | def save_json(object, handle, indent=2):
"""Save object as json on CNS."""
obj_json = json.dumps(object, indent=indent, cls=NumpyJSONEncoder)
handle.write(obj_json) | def save_json(object, handle, indent=2):
"""Save object as json on CNS."""
obj_json = json.dumps(object, indent=indent, cls=NumpyJSONEncoder)
handle.write(obj_json) | [
"Save",
"object",
"as",
"json",
"on",
"CNS",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/saving.py#L58-L61 | [
"def",
"save_json",
"(",
"object",
",",
"handle",
",",
"indent",
"=",
"2",
")",
":",
"obj_json",
"=",
"json",
".",
"dumps",
"(",
"object",
",",
"indent",
"=",
"indent",
",",
"cls",
"=",
"NumpyJSONEncoder",
")",
"handle",
".",
"write",
"(",
"obj_json",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | save_npz | Save dict of numpy array as npz file. | lucid/misc/io/saving.py | def save_npz(object, handle):
"""Save dict of numpy array as npz file."""
# there is a bug where savez doesn't actually accept a file handle.
log.warning("Saving npz files currently only works locally. :/")
path = handle.name
handle.close()
if type(object) is dict:
np.savez(path, **object)
elif type(object) is list:
np.savez(path, *object)
else:
log.warning("Saving non dict or list as npz file, did you maybe want npy?")
np.savez(path, object) | def save_npz(object, handle):
"""Save dict of numpy array as npz file."""
# there is a bug where savez doesn't actually accept a file handle.
log.warning("Saving npz files currently only works locally. :/")
path = handle.name
handle.close()
if type(object) is dict:
np.savez(path, **object)
elif type(object) is list:
np.savez(path, *object)
else:
log.warning("Saving non dict or list as npz file, did you maybe want npy?")
np.savez(path, object) | [
"Save",
"dict",
"of",
"numpy",
"array",
"as",
"npz",
"file",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/saving.py#L69-L81 | [
"def",
"save_npz",
"(",
"object",
",",
"handle",
")",
":",
"# there is a bug where savez doesn't actually accept a file handle.",
"log",
".",
"warning",
"(",
"\"Saving npz files currently only works locally. :/\"",
")",
"path",
"=",
"handle",
".",
"name",
"handle",
".",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | save_img | Save numpy array as image file on CNS. | lucid/misc/io/saving.py | def save_img(object, handle, **kwargs):
"""Save numpy array as image file on CNS."""
if isinstance(object, np.ndarray):
normalized = _normalize_array(object)
object = PIL.Image.fromarray(normalized)
if isinstance(object, PIL.Image.Image):
object.save(handle, **kwargs) # will infer format from handle's url ext.
else:
raise ValueError("Can only save_img for numpy arrays or PIL.Images!") | def save_img(object, handle, **kwargs):
"""Save numpy array as image file on CNS."""
if isinstance(object, np.ndarray):
normalized = _normalize_array(object)
object = PIL.Image.fromarray(normalized)
if isinstance(object, PIL.Image.Image):
object.save(handle, **kwargs) # will infer format from handle's url ext.
else:
raise ValueError("Can only save_img for numpy arrays or PIL.Images!") | [
"Save",
"numpy",
"array",
"as",
"image",
"file",
"on",
"CNS",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/saving.py#L84-L94 | [
"def",
"save_img",
"(",
"object",
",",
"handle",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"isinstance",
"(",
"object",
",",
"np",
".",
"ndarray",
")",
":",
"normalized",
"=",
"_normalize_array",
"(",
"object",
")",
"object",
"=",
"PIL",
".",
"Image",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | save | Save object to file on CNS.
File format is inferred from path. Use save_img(), save_npy(), or save_json()
if you need to force a particular format.
Args:
obj: object to save.
path: CNS path.
Raises:
RuntimeError: If file extension not supported. | lucid/misc/io/saving.py | def save(thing, url_or_handle, **kwargs):
"""Save object to file on CNS.
File format is inferred from path. Use save_img(), save_npy(), or save_json()
if you need to force a particular format.
Args:
obj: object to save.
path: CNS path.
Raises:
RuntimeError: If file extension not supported.
"""
is_handle = hasattr(url_or_handle, "write") and hasattr(url_or_handle, "name")
if is_handle:
_, ext = os.path.splitext(url_or_handle.name)
else:
_, ext = os.path.splitext(url_or_handle)
if not ext:
raise RuntimeError("No extension in URL: " + url_or_handle)
if ext in savers:
saver = savers[ext]
if is_handle:
saver(thing, url_or_handle, **kwargs)
else:
with write_handle(url_or_handle) as handle:
saver(thing, handle, **kwargs)
else:
saver_names = [(key, fn.__name__) for (key, fn) in savers.items()]
message = "Unknown extension '{}', supports {}."
raise ValueError(message.format(ext, saver_names)) | def save(thing, url_or_handle, **kwargs):
"""Save object to file on CNS.
File format is inferred from path. Use save_img(), save_npy(), or save_json()
if you need to force a particular format.
Args:
obj: object to save.
path: CNS path.
Raises:
RuntimeError: If file extension not supported.
"""
is_handle = hasattr(url_or_handle, "write") and hasattr(url_or_handle, "name")
if is_handle:
_, ext = os.path.splitext(url_or_handle.name)
else:
_, ext = os.path.splitext(url_or_handle)
if not ext:
raise RuntimeError("No extension in URL: " + url_or_handle)
if ext in savers:
saver = savers[ext]
if is_handle:
saver(thing, url_or_handle, **kwargs)
else:
with write_handle(url_or_handle) as handle:
saver(thing, handle, **kwargs)
else:
saver_names = [(key, fn.__name__) for (key, fn) in savers.items()]
message = "Unknown extension '{}', supports {}."
raise ValueError(message.format(ext, saver_names)) | [
"Save",
"object",
"to",
"file",
"on",
"CNS",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/saving.py#L135-L166 | [
"def",
"save",
"(",
"thing",
",",
"url_or_handle",
",",
"*",
"*",
"kwargs",
")",
":",
"is_handle",
"=",
"hasattr",
"(",
"url_or_handle",
",",
"\"write\"",
")",
"and",
"hasattr",
"(",
"url_or_handle",
",",
"\"name\"",
")",
"if",
"is_handle",
":",
"_",
","... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | frustum | Create view frustum matrix. | lucid/misc/gl/meshutil.py | def frustum(left, right, bottom, top, znear, zfar):
"""Create view frustum matrix."""
assert right != left
assert bottom != top
assert znear != zfar
M = np.zeros((4, 4), dtype=np.float32)
M[0, 0] = +2.0 * znear / (right - left)
M[2, 0] = (right + left) / (right - left)
M[1, 1] = +2.0 * znear / (top - bottom)
M[3, 1] = (top + bottom) / (top - bottom)
M[2, 2] = -(zfar + znear) / (zfar - znear)
M[3, 2] = -2.0 * znear * zfar / (zfar - znear)
M[2, 3] = -1.0
return M | def frustum(left, right, bottom, top, znear, zfar):
"""Create view frustum matrix."""
assert right != left
assert bottom != top
assert znear != zfar
M = np.zeros((4, 4), dtype=np.float32)
M[0, 0] = +2.0 * znear / (right - left)
M[2, 0] = (right + left) / (right - left)
M[1, 1] = +2.0 * znear / (top - bottom)
M[3, 1] = (top + bottom) / (top - bottom)
M[2, 2] = -(zfar + znear) / (zfar - znear)
M[3, 2] = -2.0 * znear * zfar / (zfar - znear)
M[2, 3] = -1.0
return M | [
"Create",
"view",
"frustum",
"matrix",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L8-L22 | [
"def",
"frustum",
"(",
"left",
",",
"right",
",",
"bottom",
",",
"top",
",",
"znear",
",",
"zfar",
")",
":",
"assert",
"right",
"!=",
"left",
"assert",
"bottom",
"!=",
"top",
"assert",
"znear",
"!=",
"zfar",
"M",
"=",
"np",
".",
"zeros",
"(",
"(",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | anorm | Compute L2 norms alogn specified axes. | lucid/misc/gl/meshutil.py | def anorm(x, axis=None, keepdims=False):
"""Compute L2 norms alogn specified axes."""
return np.sqrt((x*x).sum(axis=axis, keepdims=keepdims)) | def anorm(x, axis=None, keepdims=False):
"""Compute L2 norms alogn specified axes."""
return np.sqrt((x*x).sum(axis=axis, keepdims=keepdims)) | [
"Compute",
"L2",
"norms",
"alogn",
"specified",
"axes",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L33-L35 | [
"def",
"anorm",
"(",
"x",
",",
"axis",
"=",
"None",
",",
"keepdims",
"=",
"False",
")",
":",
"return",
"np",
".",
"sqrt",
"(",
"(",
"x",
"*",
"x",
")",
".",
"sum",
"(",
"axis",
"=",
"axis",
",",
"keepdims",
"=",
"keepdims",
")",
")"
] | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | normalize | L2 Normalize along specified axes. | lucid/misc/gl/meshutil.py | def normalize(v, axis=None, eps=1e-10):
"""L2 Normalize along specified axes."""
return v / max(anorm(v, axis=axis, keepdims=True), eps) | def normalize(v, axis=None, eps=1e-10):
"""L2 Normalize along specified axes."""
return v / max(anorm(v, axis=axis, keepdims=True), eps) | [
"L2",
"Normalize",
"along",
"specified",
"axes",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L38-L40 | [
"def",
"normalize",
"(",
"v",
",",
"axis",
"=",
"None",
",",
"eps",
"=",
"1e-10",
")",
":",
"return",
"v",
"/",
"max",
"(",
"anorm",
"(",
"v",
",",
"axis",
"=",
"axis",
",",
"keepdims",
"=",
"True",
")",
",",
"eps",
")"
] | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | lookat | Generate LookAt modelview matrix. | lucid/misc/gl/meshutil.py | def lookat(eye, target=[0, 0, 0], up=[0, 1, 0]):
"""Generate LookAt modelview matrix."""
eye = np.float32(eye)
forward = normalize(target - eye)
side = normalize(np.cross(forward, up))
up = np.cross(side, forward)
M = np.eye(4, dtype=np.float32)
R = M[:3, :3]
R[:] = [side, up, -forward]
M[:3, 3] = -R.dot(eye)
return M | def lookat(eye, target=[0, 0, 0], up=[0, 1, 0]):
"""Generate LookAt modelview matrix."""
eye = np.float32(eye)
forward = normalize(target - eye)
side = normalize(np.cross(forward, up))
up = np.cross(side, forward)
M = np.eye(4, dtype=np.float32)
R = M[:3, :3]
R[:] = [side, up, -forward]
M[:3, 3] = -R.dot(eye)
return M | [
"Generate",
"LookAt",
"modelview",
"matrix",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L43-L53 | [
"def",
"lookat",
"(",
"eye",
",",
"target",
"=",
"[",
"0",
",",
"0",
",",
"0",
"]",
",",
"up",
"=",
"[",
"0",
",",
"1",
",",
"0",
"]",
")",
":",
"eye",
"=",
"np",
".",
"float32",
"(",
"eye",
")",
"forward",
"=",
"normalize",
"(",
"target",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | sample_view | Sample random camera position.
Sample origin directed camera position in given distance
range from the origin. ModelView matrix is returned. | lucid/misc/gl/meshutil.py | def sample_view(min_dist, max_dist=None):
'''Sample random camera position.
Sample origin directed camera position in given distance
range from the origin. ModelView matrix is returned.
'''
if max_dist is None:
max_dist = min_dist
dist = np.random.uniform(min_dist, max_dist)
eye = np.random.normal(size=3)
eye = normalize(eye)*dist
return lookat(eye) | def sample_view(min_dist, max_dist=None):
'''Sample random camera position.
Sample origin directed camera position in given distance
range from the origin. ModelView matrix is returned.
'''
if max_dist is None:
max_dist = min_dist
dist = np.random.uniform(min_dist, max_dist)
eye = np.random.normal(size=3)
eye = normalize(eye)*dist
return lookat(eye) | [
"Sample",
"random",
"camera",
"position",
".",
"Sample",
"origin",
"directed",
"camera",
"position",
"in",
"given",
"distance",
"range",
"from",
"the",
"origin",
".",
"ModelView",
"matrix",
"is",
"returned",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L56-L67 | [
"def",
"sample_view",
"(",
"min_dist",
",",
"max_dist",
"=",
"None",
")",
":",
"if",
"max_dist",
"is",
"None",
":",
"max_dist",
"=",
"min_dist",
"dist",
"=",
"np",
".",
"random",
".",
"uniform",
"(",
"min_dist",
",",
"max_dist",
")",
"eye",
"=",
"np",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _parse_vertex_tuple | Parse vertex indices in '/' separated form (like 'i/j/k', 'i//k' ...). | lucid/misc/gl/meshutil.py | def _parse_vertex_tuple(s):
"""Parse vertex indices in '/' separated form (like 'i/j/k', 'i//k' ...)."""
vt = [0, 0, 0]
for i, c in enumerate(s.split('/')):
if c:
vt[i] = int(c)
return tuple(vt) | def _parse_vertex_tuple(s):
"""Parse vertex indices in '/' separated form (like 'i/j/k', 'i//k' ...)."""
vt = [0, 0, 0]
for i, c in enumerate(s.split('/')):
if c:
vt[i] = int(c)
return tuple(vt) | [
"Parse",
"vertex",
"indices",
"in",
"/",
"separated",
"form",
"(",
"like",
"i",
"/",
"j",
"/",
"k",
"i",
"//",
"k",
"...",
")",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L78-L84 | [
"def",
"_parse_vertex_tuple",
"(",
"s",
")",
":",
"vt",
"=",
"[",
"0",
",",
"0",
",",
"0",
"]",
"for",
"i",
",",
"c",
"in",
"enumerate",
"(",
"s",
".",
"split",
"(",
"'/'",
")",
")",
":",
"if",
"c",
":",
"vt",
"[",
"i",
"]",
"=",
"int",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _unify_rows | Unify lengths of each row of a. | lucid/misc/gl/meshutil.py | def _unify_rows(a):
"""Unify lengths of each row of a."""
lens = np.fromiter(map(len, a), np.int32)
if not (lens[0] == lens).all():
out = np.zeros((len(a), lens.max()), np.float32)
for i, row in enumerate(a):
out[i, :lens[i]] = row
else:
out = np.float32(a)
return out | def _unify_rows(a):
"""Unify lengths of each row of a."""
lens = np.fromiter(map(len, a), np.int32)
if not (lens[0] == lens).all():
out = np.zeros((len(a), lens.max()), np.float32)
for i, row in enumerate(a):
out[i, :lens[i]] = row
else:
out = np.float32(a)
return out | [
"Unify",
"lengths",
"of",
"each",
"row",
"of",
"a",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L87-L96 | [
"def",
"_unify_rows",
"(",
"a",
")",
":",
"lens",
"=",
"np",
".",
"fromiter",
"(",
"map",
"(",
"len",
",",
"a",
")",
",",
"np",
".",
"int32",
")",
"if",
"not",
"(",
"lens",
"[",
"0",
"]",
"==",
"lens",
")",
".",
"all",
"(",
")",
":",
"out",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | load_obj | Load 3d mesh form .obj' file.
Args:
fn: Input file name or file-like object.
Returns:
dictionary with the following keys (some of which may be missing):
position: np.float32, (n, 3) array, vertex positions
uv: np.float32, (n, 2) array, vertex uv coordinates
normal: np.float32, (n, 3) array, vertex uv normals
face: np.int32, (k*3,) traingular face indices | lucid/misc/gl/meshutil.py | def load_obj(fn):
"""Load 3d mesh form .obj' file.
Args:
fn: Input file name or file-like object.
Returns:
dictionary with the following keys (some of which may be missing):
position: np.float32, (n, 3) array, vertex positions
uv: np.float32, (n, 2) array, vertex uv coordinates
normal: np.float32, (n, 3) array, vertex uv normals
face: np.int32, (k*3,) traingular face indices
"""
position = [np.zeros(3, dtype=np.float32)]
normal = [np.zeros(3, dtype=np.float32)]
uv = [np.zeros(2, dtype=np.float32)]
tuple2idx = OrderedDict()
trinagle_indices = []
input_file = open(fn) if isinstance(fn, str) else fn
for line in input_file:
line = line.strip()
if not line or line[0] == '#':
continue
line = line.split(' ', 1)
tag = line[0]
if len(line) > 1:
line = line[1]
else:
line = ''
if tag == 'v':
position.append(np.fromstring(line, sep=' '))
elif tag == 'vt':
uv.append(np.fromstring(line, sep=' '))
elif tag == 'vn':
normal.append(np.fromstring(line, sep=' '))
elif tag == 'f':
output_face_indices = []
for chunk in line.split():
# tuple order: pos_idx, uv_idx, normal_idx
vt = _parse_vertex_tuple(chunk)
if vt not in tuple2idx: # create a new output vertex?
tuple2idx[vt] = len(tuple2idx)
output_face_indices.append(tuple2idx[vt])
# generate face triangles
for i in range(1, len(output_face_indices)-1):
for vi in [0, i, i+1]:
trinagle_indices.append(output_face_indices[vi])
outputs = {}
outputs['face'] = np.int32(trinagle_indices)
pos_idx, uv_idx, normal_idx = np.int32(list(tuple2idx)).T
if np.any(pos_idx):
outputs['position'] = _unify_rows(position)[pos_idx]
if np.any(uv_idx):
outputs['uv'] = _unify_rows(uv)[uv_idx]
if np.any(normal_idx):
outputs['normal'] = _unify_rows(normal)[normal_idx]
return outputs | def load_obj(fn):
"""Load 3d mesh form .obj' file.
Args:
fn: Input file name or file-like object.
Returns:
dictionary with the following keys (some of which may be missing):
position: np.float32, (n, 3) array, vertex positions
uv: np.float32, (n, 2) array, vertex uv coordinates
normal: np.float32, (n, 3) array, vertex uv normals
face: np.int32, (k*3,) traingular face indices
"""
position = [np.zeros(3, dtype=np.float32)]
normal = [np.zeros(3, dtype=np.float32)]
uv = [np.zeros(2, dtype=np.float32)]
tuple2idx = OrderedDict()
trinagle_indices = []
input_file = open(fn) if isinstance(fn, str) else fn
for line in input_file:
line = line.strip()
if not line or line[0] == '#':
continue
line = line.split(' ', 1)
tag = line[0]
if len(line) > 1:
line = line[1]
else:
line = ''
if tag == 'v':
position.append(np.fromstring(line, sep=' '))
elif tag == 'vt':
uv.append(np.fromstring(line, sep=' '))
elif tag == 'vn':
normal.append(np.fromstring(line, sep=' '))
elif tag == 'f':
output_face_indices = []
for chunk in line.split():
# tuple order: pos_idx, uv_idx, normal_idx
vt = _parse_vertex_tuple(chunk)
if vt not in tuple2idx: # create a new output vertex?
tuple2idx[vt] = len(tuple2idx)
output_face_indices.append(tuple2idx[vt])
# generate face triangles
for i in range(1, len(output_face_indices)-1):
for vi in [0, i, i+1]:
trinagle_indices.append(output_face_indices[vi])
outputs = {}
outputs['face'] = np.int32(trinagle_indices)
pos_idx, uv_idx, normal_idx = np.int32(list(tuple2idx)).T
if np.any(pos_idx):
outputs['position'] = _unify_rows(position)[pos_idx]
if np.any(uv_idx):
outputs['uv'] = _unify_rows(uv)[uv_idx]
if np.any(normal_idx):
outputs['normal'] = _unify_rows(normal)[normal_idx]
return outputs | [
"Load",
"3d",
"mesh",
"form",
".",
"obj",
"file",
".",
"Args",
":",
"fn",
":",
"Input",
"file",
"name",
"or",
"file",
"-",
"like",
"object",
".",
"Returns",
":",
"dictionary",
"with",
"the",
"following",
"keys",
"(",
"some",
"of",
"which",
"may",
"be... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L99-L158 | [
"def",
"load_obj",
"(",
"fn",
")",
":",
"position",
"=",
"[",
"np",
".",
"zeros",
"(",
"3",
",",
"dtype",
"=",
"np",
".",
"float32",
")",
"]",
"normal",
"=",
"[",
"np",
".",
"zeros",
"(",
"3",
",",
"dtype",
"=",
"np",
".",
"float32",
")",
"]"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | normalize_mesh | Scale mesh to fit into -1..1 cube | lucid/misc/gl/meshutil.py | def normalize_mesh(mesh):
'''Scale mesh to fit into -1..1 cube'''
mesh = dict(mesh)
pos = mesh['position'][:,:3].copy()
pos -= (pos.max(0)+pos.min(0)) / 2.0
pos /= np.abs(pos).max()
mesh['position'] = pos
return mesh | def normalize_mesh(mesh):
'''Scale mesh to fit into -1..1 cube'''
mesh = dict(mesh)
pos = mesh['position'][:,:3].copy()
pos -= (pos.max(0)+pos.min(0)) / 2.0
pos /= np.abs(pos).max()
mesh['position'] = pos
return mesh | [
"Scale",
"mesh",
"to",
"fit",
"into",
"-",
"1",
"..",
"1",
"cube"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/meshutil.py#L161-L168 | [
"def",
"normalize_mesh",
"(",
"mesh",
")",
":",
"mesh",
"=",
"dict",
"(",
"mesh",
")",
"pos",
"=",
"mesh",
"[",
"'position'",
"]",
"[",
":",
",",
":",
"3",
"]",
".",
"copy",
"(",
")",
"pos",
"-=",
"(",
"pos",
".",
"max",
"(",
"0",
")",
"+",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | Layer.activations | Loads sampled activations, which requires network access. | lucid/modelzoo/vision_base.py | def activations(self):
"""Loads sampled activations, which requires network access."""
if self._activations is None:
self._activations = _get_aligned_activations(self)
return self._activations | def activations(self):
"""Loads sampled activations, which requires network access."""
if self._activations is None:
self._activations = _get_aligned_activations(self)
return self._activations | [
"Loads",
"sampled",
"activations",
"which",
"requires",
"network",
"access",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/vision_base.py#L71-L75 | [
"def",
"activations",
"(",
"self",
")",
":",
"if",
"self",
".",
"_activations",
"is",
"None",
":",
"self",
".",
"_activations",
"=",
"_get_aligned_activations",
"(",
"self",
")",
"return",
"self",
".",
"_activations"
] | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | Model.create_input | Create input tensor. | lucid/modelzoo/vision_base.py | def create_input(self, t_input=None, forget_xy_shape=True):
"""Create input tensor."""
if t_input is None:
t_input = tf.placeholder(tf.float32, self.image_shape)
t_prep_input = t_input
if len(t_prep_input.shape) == 3:
t_prep_input = tf.expand_dims(t_prep_input, 0)
if forget_xy_shape:
t_prep_input = model_util.forget_xy(t_prep_input)
if hasattr(self, "is_BGR") and self.is_BGR is True:
t_prep_input = tf.reverse(t_prep_input, [-1])
lo, hi = self.image_value_range
t_prep_input = lo + t_prep_input * (hi - lo)
return t_input, t_prep_input | def create_input(self, t_input=None, forget_xy_shape=True):
"""Create input tensor."""
if t_input is None:
t_input = tf.placeholder(tf.float32, self.image_shape)
t_prep_input = t_input
if len(t_prep_input.shape) == 3:
t_prep_input = tf.expand_dims(t_prep_input, 0)
if forget_xy_shape:
t_prep_input = model_util.forget_xy(t_prep_input)
if hasattr(self, "is_BGR") and self.is_BGR is True:
t_prep_input = tf.reverse(t_prep_input, [-1])
lo, hi = self.image_value_range
t_prep_input = lo + t_prep_input * (hi - lo)
return t_input, t_prep_input | [
"Create",
"input",
"tensor",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/vision_base.py#L161-L174 | [
"def",
"create_input",
"(",
"self",
",",
"t_input",
"=",
"None",
",",
"forget_xy_shape",
"=",
"True",
")",
":",
"if",
"t_input",
"is",
"None",
":",
"t_input",
"=",
"tf",
".",
"placeholder",
"(",
"tf",
".",
"float32",
",",
"self",
".",
"image_shape",
")... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | Model.import_graph | Import model GraphDef into the current graph. | lucid/modelzoo/vision_base.py | def import_graph(self, t_input=None, scope='import', forget_xy_shape=True):
"""Import model GraphDef into the current graph."""
graph = tf.get_default_graph()
assert graph.unique_name(scope, False) == scope, (
'Scope "%s" already exists. Provide explicit scope names when '
'importing multiple instances of the model.') % scope
t_input, t_prep_input = self.create_input(t_input, forget_xy_shape)
tf.import_graph_def(
self.graph_def, {self.input_name: t_prep_input}, name=scope)
self.post_import(scope) | def import_graph(self, t_input=None, scope='import', forget_xy_shape=True):
"""Import model GraphDef into the current graph."""
graph = tf.get_default_graph()
assert graph.unique_name(scope, False) == scope, (
'Scope "%s" already exists. Provide explicit scope names when '
'importing multiple instances of the model.') % scope
t_input, t_prep_input = self.create_input(t_input, forget_xy_shape)
tf.import_graph_def(
self.graph_def, {self.input_name: t_prep_input}, name=scope)
self.post_import(scope) | [
"Import",
"model",
"GraphDef",
"into",
"the",
"current",
"graph",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/vision_base.py#L176-L185 | [
"def",
"import_graph",
"(",
"self",
",",
"t_input",
"=",
"None",
",",
"scope",
"=",
"'import'",
",",
"forget_xy_shape",
"=",
"True",
")",
":",
"graph",
"=",
"tf",
".",
"get_default_graph",
"(",
")",
"assert",
"graph",
".",
"unique_name",
"(",
"scope",
",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | normalize_layout | Removes outliers and scales layout to between [0,1]. | lucid/recipes/activation_atlas/layout.py | def normalize_layout(layout, min_percentile=1, max_percentile=99, relative_margin=0.1):
"""Removes outliers and scales layout to between [0,1]."""
# compute percentiles
mins = np.percentile(layout, min_percentile, axis=(0))
maxs = np.percentile(layout, max_percentile, axis=(0))
# add margins
mins -= relative_margin * (maxs - mins)
maxs += relative_margin * (maxs - mins)
# `clip` broadcasts, `[None]`s added only for readability
clipped = np.clip(layout, mins, maxs)
# embed within [0,1] along both axes
clipped -= clipped.min(axis=0)
clipped /= clipped.max(axis=0)
return clipped | def normalize_layout(layout, min_percentile=1, max_percentile=99, relative_margin=0.1):
"""Removes outliers and scales layout to between [0,1]."""
# compute percentiles
mins = np.percentile(layout, min_percentile, axis=(0))
maxs = np.percentile(layout, max_percentile, axis=(0))
# add margins
mins -= relative_margin * (maxs - mins)
maxs += relative_margin * (maxs - mins)
# `clip` broadcasts, `[None]`s added only for readability
clipped = np.clip(layout, mins, maxs)
# embed within [0,1] along both axes
clipped -= clipped.min(axis=0)
clipped /= clipped.max(axis=0)
return clipped | [
"Removes",
"outliers",
"and",
"scales",
"layout",
"to",
"between",
"[",
"0",
"1",
"]",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/recipes/activation_atlas/layout.py#L25-L43 | [
"def",
"normalize_layout",
"(",
"layout",
",",
"min_percentile",
"=",
"1",
",",
"max_percentile",
"=",
"99",
",",
"relative_margin",
"=",
"0.1",
")",
":",
"# compute percentiles",
"mins",
"=",
"np",
".",
"percentile",
"(",
"layout",
",",
"min_percentile",
",",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | aligned_umap | `activations` can be a list of ndarrays. In that case a list of layouts is returned. | lucid/recipes/activation_atlas/layout.py | def aligned_umap(activations, umap_options={}, normalize=True, verbose=False):
"""`activations` can be a list of ndarrays. In that case a list of layouts is returned."""
umap_defaults = dict(
n_components=2, n_neighbors=50, min_dist=0.05, verbose=verbose, metric="cosine"
)
umap_defaults.update(umap_options)
# if passed a list of activations, we combine them and later split the layouts
if type(activations) is list or type(activations) is tuple:
num_activation_groups = len(activations)
combined_activations = np.concatenate(activations)
else:
num_activation_groups = 1
combined_activations = activations
try:
layout = UMAP(**umap_defaults).fit_transform(combined_activations)
except (RecursionError, SystemError) as exception:
log.error("UMAP failed to fit these activations. We're not yet sure why this sometimes occurs.")
raise ValueError("UMAP failed to fit activations: %s", exception)
if normalize:
layout = normalize_layout(layout)
if num_activation_groups > 1:
layouts = np.split(layout, num_activation_groups, axis=0)
return layouts
else:
return layout | def aligned_umap(activations, umap_options={}, normalize=True, verbose=False):
"""`activations` can be a list of ndarrays. In that case a list of layouts is returned."""
umap_defaults = dict(
n_components=2, n_neighbors=50, min_dist=0.05, verbose=verbose, metric="cosine"
)
umap_defaults.update(umap_options)
# if passed a list of activations, we combine them and later split the layouts
if type(activations) is list or type(activations) is tuple:
num_activation_groups = len(activations)
combined_activations = np.concatenate(activations)
else:
num_activation_groups = 1
combined_activations = activations
try:
layout = UMAP(**umap_defaults).fit_transform(combined_activations)
except (RecursionError, SystemError) as exception:
log.error("UMAP failed to fit these activations. We're not yet sure why this sometimes occurs.")
raise ValueError("UMAP failed to fit activations: %s", exception)
if normalize:
layout = normalize_layout(layout)
if num_activation_groups > 1:
layouts = np.split(layout, num_activation_groups, axis=0)
return layouts
else:
return layout | [
"activations",
"can",
"be",
"a",
"list",
"of",
"ndarrays",
".",
"In",
"that",
"case",
"a",
"list",
"of",
"layouts",
"is",
"returned",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/recipes/activation_atlas/layout.py#L46-L74 | [
"def",
"aligned_umap",
"(",
"activations",
",",
"umap_options",
"=",
"{",
"}",
",",
"normalize",
"=",
"True",
",",
"verbose",
"=",
"False",
")",
":",
"umap_defaults",
"=",
"dict",
"(",
"n_components",
"=",
"2",
",",
"n_neighbors",
"=",
"50",
",",
"min_di... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | render_tile | Render each cell in the tile and stitch it into a single image | lucid/scratch/atlas_pipeline/render_tile.py | def render_tile(cells, ti, tj, render, params, metadata, layout, summary):
"""
Render each cell in the tile and stitch it into a single image
"""
image_size = params["cell_size"] * params["n_tile"]
tile = Image.new("RGB", (image_size, image_size), (255,255,255))
keys = cells.keys()
for i,key in enumerate(keys):
print("cell", i+1, "/", len(keys), end='\r')
cell_image = render(cells[key], params, metadata, layout, summary)
# stitch this rendering into the tile image
ci = key[0] % params["n_tile"]
cj = key[1] % params["n_tile"]
xmin = ci*params["cell_size"]
ymin = cj*params["cell_size"]
xmax = (ci+1)*params["cell_size"]
ymax = (cj+1)*params["cell_size"]
if params.get("scale_density", False):
density = len(cells[key]["gi"])
# scale = density/summary["max_density"]
scale = math.log(density)/(math.log(summary["max_density"]) or 1)
owidth = xmax - xmin
width = int(round(owidth * scale))
if(width < 1):
width = 1
offsetL = int(round((owidth - width)/2))
offsetR = owidth - width - offsetL # handle odd numbers
# print("\n")
# print("width", width, offsetL, offsetR)
box = [xmin + offsetL, ymin + offsetL, xmax - offsetR, ymax - offsetR]
resample = params.get("scale_type", Image.NEAREST)
cell_image = cell_image.resize(size=(width,width), resample=resample)
# print(cell_image)
else:
box = [xmin, ymin, xmax, ymax]
# print("box", box)
tile.paste(cell_image, box)
print("\n")
return tile | def render_tile(cells, ti, tj, render, params, metadata, layout, summary):
"""
Render each cell in the tile and stitch it into a single image
"""
image_size = params["cell_size"] * params["n_tile"]
tile = Image.new("RGB", (image_size, image_size), (255,255,255))
keys = cells.keys()
for i,key in enumerate(keys):
print("cell", i+1, "/", len(keys), end='\r')
cell_image = render(cells[key], params, metadata, layout, summary)
# stitch this rendering into the tile image
ci = key[0] % params["n_tile"]
cj = key[1] % params["n_tile"]
xmin = ci*params["cell_size"]
ymin = cj*params["cell_size"]
xmax = (ci+1)*params["cell_size"]
ymax = (cj+1)*params["cell_size"]
if params.get("scale_density", False):
density = len(cells[key]["gi"])
# scale = density/summary["max_density"]
scale = math.log(density)/(math.log(summary["max_density"]) or 1)
owidth = xmax - xmin
width = int(round(owidth * scale))
if(width < 1):
width = 1
offsetL = int(round((owidth - width)/2))
offsetR = owidth - width - offsetL # handle odd numbers
# print("\n")
# print("width", width, offsetL, offsetR)
box = [xmin + offsetL, ymin + offsetL, xmax - offsetR, ymax - offsetR]
resample = params.get("scale_type", Image.NEAREST)
cell_image = cell_image.resize(size=(width,width), resample=resample)
# print(cell_image)
else:
box = [xmin, ymin, xmax, ymax]
# print("box", box)
tile.paste(cell_image, box)
print("\n")
return tile | [
"Render",
"each",
"cell",
"in",
"the",
"tile",
"and",
"stitch",
"it",
"into",
"a",
"single",
"image"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/scratch/atlas_pipeline/render_tile.py#L11-L51 | [
"def",
"render_tile",
"(",
"cells",
",",
"ti",
",",
"tj",
",",
"render",
",",
"params",
",",
"metadata",
",",
"layout",
",",
"summary",
")",
":",
"image_size",
"=",
"params",
"[",
"\"cell_size\"",
"]",
"*",
"params",
"[",
"\"n_tile\"",
"]",
"tile",
"="... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | aggregate_tile | Call the user defined aggregation function on each cell and combine into a single json object | lucid/scratch/atlas_pipeline/render_tile.py | def aggregate_tile(cells, ti, tj, aggregate, params, metadata, layout, summary):
"""
Call the user defined aggregation function on each cell and combine into a single json object
"""
tile = []
keys = cells.keys()
for i,key in enumerate(keys):
print("cell", i+1, "/", len(keys), end='\r')
cell_json = aggregate(cells[key], params, metadata, layout, summary)
tile.append({"aggregate":cell_json, "i":int(key[0]), "j":int(key[1])})
return tile | def aggregate_tile(cells, ti, tj, aggregate, params, metadata, layout, summary):
"""
Call the user defined aggregation function on each cell and combine into a single json object
"""
tile = []
keys = cells.keys()
for i,key in enumerate(keys):
print("cell", i+1, "/", len(keys), end='\r')
cell_json = aggregate(cells[key], params, metadata, layout, summary)
tile.append({"aggregate":cell_json, "i":int(key[0]), "j":int(key[1])})
return tile | [
"Call",
"the",
"user",
"defined",
"aggregation",
"function",
"on",
"each",
"cell",
"and",
"combine",
"into",
"a",
"single",
"json",
"object"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/scratch/atlas_pipeline/render_tile.py#L54-L64 | [
"def",
"aggregate_tile",
"(",
"cells",
",",
"ti",
",",
"tj",
",",
"aggregate",
",",
"params",
",",
"metadata",
",",
"layout",
",",
"summary",
")",
":",
"tile",
"=",
"[",
"]",
"keys",
"=",
"cells",
".",
"keys",
"(",
")",
"for",
"i",
",",
"key",
"i... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | create_opengl_context | Create offscreen OpenGL context and make it current.
Users are expected to directly use EGL API in case more advanced
context management is required.
Args:
surface_size: (width, height), size of the offscreen rendering surface. | lucid/misc/gl/glcontext.py | def create_opengl_context(surface_size=(640, 480)):
"""Create offscreen OpenGL context and make it current.
Users are expected to directly use EGL API in case more advanced
context management is required.
Args:
surface_size: (width, height), size of the offscreen rendering surface.
"""
egl_display = egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY)
major, minor = egl.EGLint(), egl.EGLint()
egl.eglInitialize(egl_display, pointer(major), pointer(minor))
config_attribs = [
egl.EGL_SURFACE_TYPE, egl.EGL_PBUFFER_BIT, egl.EGL_BLUE_SIZE, 8,
egl.EGL_GREEN_SIZE, 8, egl.EGL_RED_SIZE, 8, egl.EGL_DEPTH_SIZE, 24,
egl.EGL_RENDERABLE_TYPE, egl.EGL_OPENGL_BIT, egl.EGL_NONE
]
config_attribs = (egl.EGLint * len(config_attribs))(*config_attribs)
num_configs = egl.EGLint()
egl_cfg = egl.EGLConfig()
egl.eglChooseConfig(egl_display, config_attribs, pointer(egl_cfg), 1,
pointer(num_configs))
width, height = surface_size
pbuffer_attribs = [
egl.EGL_WIDTH,
width,
egl.EGL_HEIGHT,
height,
egl.EGL_NONE,
]
pbuffer_attribs = (egl.EGLint * len(pbuffer_attribs))(*pbuffer_attribs)
egl_surf = egl.eglCreatePbufferSurface(egl_display, egl_cfg, pbuffer_attribs)
egl.eglBindAPI(egl.EGL_OPENGL_API)
egl_context = egl.eglCreateContext(egl_display, egl_cfg, egl.EGL_NO_CONTEXT,
None)
egl.eglMakeCurrent(egl_display, egl_surf, egl_surf, egl_context) | def create_opengl_context(surface_size=(640, 480)):
"""Create offscreen OpenGL context and make it current.
Users are expected to directly use EGL API in case more advanced
context management is required.
Args:
surface_size: (width, height), size of the offscreen rendering surface.
"""
egl_display = egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY)
major, minor = egl.EGLint(), egl.EGLint()
egl.eglInitialize(egl_display, pointer(major), pointer(minor))
config_attribs = [
egl.EGL_SURFACE_TYPE, egl.EGL_PBUFFER_BIT, egl.EGL_BLUE_SIZE, 8,
egl.EGL_GREEN_SIZE, 8, egl.EGL_RED_SIZE, 8, egl.EGL_DEPTH_SIZE, 24,
egl.EGL_RENDERABLE_TYPE, egl.EGL_OPENGL_BIT, egl.EGL_NONE
]
config_attribs = (egl.EGLint * len(config_attribs))(*config_attribs)
num_configs = egl.EGLint()
egl_cfg = egl.EGLConfig()
egl.eglChooseConfig(egl_display, config_attribs, pointer(egl_cfg), 1,
pointer(num_configs))
width, height = surface_size
pbuffer_attribs = [
egl.EGL_WIDTH,
width,
egl.EGL_HEIGHT,
height,
egl.EGL_NONE,
]
pbuffer_attribs = (egl.EGLint * len(pbuffer_attribs))(*pbuffer_attribs)
egl_surf = egl.eglCreatePbufferSurface(egl_display, egl_cfg, pbuffer_attribs)
egl.eglBindAPI(egl.EGL_OPENGL_API)
egl_context = egl.eglCreateContext(egl_display, egl_cfg, egl.EGL_NO_CONTEXT,
None)
egl.eglMakeCurrent(egl_display, egl_surf, egl_surf, egl_context) | [
"Create",
"offscreen",
"OpenGL",
"context",
"and",
"make",
"it",
"current",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/gl/glcontext.py#L79-L120 | [
"def",
"create_opengl_context",
"(",
"surface_size",
"=",
"(",
"640",
",",
"480",
")",
")",
":",
"egl_display",
"=",
"egl",
".",
"eglGetDisplay",
"(",
"egl",
".",
"EGL_DEFAULT_DISPLAY",
")",
"major",
",",
"minor",
"=",
"egl",
".",
"EGLint",
"(",
")",
","... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | collapse_shape | Collapse `shape` outside the interval (`a`,`b`).
This function collapses `shape` outside the interval (`a`,`b`) by
multiplying the dimensions before `a` into a single dimension,
and mutliplying the dimensions after `b` into a single dimension.
Args:
shape: a tensor shape
a: integer, position in shape
b: integer, position in shape
Returns:
The collapsed shape, represented as a list.
Examples:
[1, 2, 3, 4, 5], (a=0, b=2) => [1, 1, 2, 60]
[1, 2, 3, 4, 5], (a=1, b=3) => [1, 2, 3, 20]
[1, 2, 3, 4, 5], (a=2, b=4) => [2, 3, 4, 5 ]
[1, 2, 3, 4, 5], (a=3, b=5) => [6, 4, 5, 1 ] | lucid/optvis/param/resize_bilinear_nd.py | def collapse_shape(shape, a, b):
"""Collapse `shape` outside the interval (`a`,`b`).
This function collapses `shape` outside the interval (`a`,`b`) by
multiplying the dimensions before `a` into a single dimension,
and mutliplying the dimensions after `b` into a single dimension.
Args:
shape: a tensor shape
a: integer, position in shape
b: integer, position in shape
Returns:
The collapsed shape, represented as a list.
Examples:
[1, 2, 3, 4, 5], (a=0, b=2) => [1, 1, 2, 60]
[1, 2, 3, 4, 5], (a=1, b=3) => [1, 2, 3, 20]
[1, 2, 3, 4, 5], (a=2, b=4) => [2, 3, 4, 5 ]
[1, 2, 3, 4, 5], (a=3, b=5) => [6, 4, 5, 1 ]
"""
shape = list(shape)
if a < 0:
n_pad = -a
pad = n_pad * [1]
return collapse_shape(pad + shape, a + n_pad, b + n_pad)
if b > len(shape):
n_pad = b - len(shape)
pad = n_pad * [1]
return collapse_shape(shape + pad, a, b)
return [product(shape[:a])] + shape[a:b] + [product(shape[b:])] | def collapse_shape(shape, a, b):
"""Collapse `shape` outside the interval (`a`,`b`).
This function collapses `shape` outside the interval (`a`,`b`) by
multiplying the dimensions before `a` into a single dimension,
and mutliplying the dimensions after `b` into a single dimension.
Args:
shape: a tensor shape
a: integer, position in shape
b: integer, position in shape
Returns:
The collapsed shape, represented as a list.
Examples:
[1, 2, 3, 4, 5], (a=0, b=2) => [1, 1, 2, 60]
[1, 2, 3, 4, 5], (a=1, b=3) => [1, 2, 3, 20]
[1, 2, 3, 4, 5], (a=2, b=4) => [2, 3, 4, 5 ]
[1, 2, 3, 4, 5], (a=3, b=5) => [6, 4, 5, 1 ]
"""
shape = list(shape)
if a < 0:
n_pad = -a
pad = n_pad * [1]
return collapse_shape(pad + shape, a + n_pad, b + n_pad)
if b > len(shape):
n_pad = b - len(shape)
pad = n_pad * [1]
return collapse_shape(shape + pad, a, b)
return [product(shape[:a])] + shape[a:b] + [product(shape[b:])] | [
"Collapse",
"shape",
"outside",
"the",
"interval",
"(",
"a",
"b",
")",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/resize_bilinear_nd.py#L35-L65 | [
"def",
"collapse_shape",
"(",
"shape",
",",
"a",
",",
"b",
")",
":",
"shape",
"=",
"list",
"(",
"shape",
")",
"if",
"a",
"<",
"0",
":",
"n_pad",
"=",
"-",
"a",
"pad",
"=",
"n_pad",
"*",
"[",
"1",
"]",
"return",
"collapse_shape",
"(",
"pad",
"+"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | resize_bilinear_nd | Bilinear resizes a tensor t to have shape target_shape.
This function bilinearly resizes a n-dimensional tensor by iteratively
applying tf.image.resize_bilinear (which can only resize 2 dimensions).
For bilinear interpolation, the order in which it is applied does not matter.
Args:
t: tensor to be resized
target_shape: the desired shape of the new tensor.
Returns:
The resized tensor | lucid/optvis/param/resize_bilinear_nd.py | def resize_bilinear_nd(t, target_shape):
"""Bilinear resizes a tensor t to have shape target_shape.
This function bilinearly resizes a n-dimensional tensor by iteratively
applying tf.image.resize_bilinear (which can only resize 2 dimensions).
For bilinear interpolation, the order in which it is applied does not matter.
Args:
t: tensor to be resized
target_shape: the desired shape of the new tensor.
Returns:
The resized tensor
"""
shape = t.get_shape().as_list()
target_shape = list(target_shape)
assert len(shape) == len(target_shape)
# We progressively move through the shape, resizing dimensions...
d = 0
while d < len(shape):
# If we don't need to deal with the next dimesnion, step over it
if shape[d] == target_shape[d]:
d += 1
continue
# Otherwise, we'll resize the next two dimensions...
# If d+2 doesn't need to be resized, this will just be a null op for it
new_shape = shape[:]
new_shape[d : d+2] = target_shape[d : d+2]
# The helper collapse_shape() makes our shapes 4-dimensional with
# the two dimesnions we want to deal with in the middle.
shape_ = collapse_shape(shape, d, d+2)
new_shape_ = collapse_shape(new_shape, d, d+2)
# We can then reshape and use the 2d tf.image.resize_bilinear() on the
# inner two dimesions.
t_ = tf.reshape(t, shape_)
t_ = tf.image.resize_bilinear(t_, new_shape_[1:3])
# And then reshape back to our uncollapsed version, having finished resizing
# two more dimensions in our shape.
t = tf.reshape(t_, new_shape)
shape = new_shape
d += 2
return t | def resize_bilinear_nd(t, target_shape):
"""Bilinear resizes a tensor t to have shape target_shape.
This function bilinearly resizes a n-dimensional tensor by iteratively
applying tf.image.resize_bilinear (which can only resize 2 dimensions).
For bilinear interpolation, the order in which it is applied does not matter.
Args:
t: tensor to be resized
target_shape: the desired shape of the new tensor.
Returns:
The resized tensor
"""
shape = t.get_shape().as_list()
target_shape = list(target_shape)
assert len(shape) == len(target_shape)
# We progressively move through the shape, resizing dimensions...
d = 0
while d < len(shape):
# If we don't need to deal with the next dimesnion, step over it
if shape[d] == target_shape[d]:
d += 1
continue
# Otherwise, we'll resize the next two dimensions...
# If d+2 doesn't need to be resized, this will just be a null op for it
new_shape = shape[:]
new_shape[d : d+2] = target_shape[d : d+2]
# The helper collapse_shape() makes our shapes 4-dimensional with
# the two dimesnions we want to deal with in the middle.
shape_ = collapse_shape(shape, d, d+2)
new_shape_ = collapse_shape(new_shape, d, d+2)
# We can then reshape and use the 2d tf.image.resize_bilinear() on the
# inner two dimesions.
t_ = tf.reshape(t, shape_)
t_ = tf.image.resize_bilinear(t_, new_shape_[1:3])
# And then reshape back to our uncollapsed version, having finished resizing
# two more dimensions in our shape.
t = tf.reshape(t_, new_shape)
shape = new_shape
d += 2
return t | [
"Bilinear",
"resizes",
"a",
"tensor",
"t",
"to",
"have",
"shape",
"target_shape",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/resize_bilinear_nd.py#L68-L116 | [
"def",
"resize_bilinear_nd",
"(",
"t",
",",
"target_shape",
")",
":",
"shape",
"=",
"t",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"target_shape",
"=",
"list",
"(",
"target_shape",
")",
"assert",
"len",
"(",
"shape",
")",
"==",
"len",
"(",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | get_aligned_activations | Downloads 100k activations of the specified layer sampled from iterating over
ImageNet. Activations of all layers where sampled at the same spatial positions for
each image, allowing the calculation of correlations. | lucid/modelzoo/aligned_activations.py | def get_aligned_activations(layer):
"""Downloads 100k activations of the specified layer sampled from iterating over
ImageNet. Activations of all layers where sampled at the same spatial positions for
each image, allowing the calculation of correlations."""
activation_paths = [
PATH_TEMPLATE.format(
sanitize(layer.model_class.name), sanitize(layer.name), page
)
for page in range(NUMBER_OF_PAGES)
]
activations = np.vstack([load(path) for path in activation_paths])
assert np.all(np.isfinite(activations))
return activations | def get_aligned_activations(layer):
"""Downloads 100k activations of the specified layer sampled from iterating over
ImageNet. Activations of all layers where sampled at the same spatial positions for
each image, allowing the calculation of correlations."""
activation_paths = [
PATH_TEMPLATE.format(
sanitize(layer.model_class.name), sanitize(layer.name), page
)
for page in range(NUMBER_OF_PAGES)
]
activations = np.vstack([load(path) for path in activation_paths])
assert np.all(np.isfinite(activations))
return activations | [
"Downloads",
"100k",
"activations",
"of",
"the",
"specified",
"layer",
"sampled",
"from",
"iterating",
"over",
"ImageNet",
".",
"Activations",
"of",
"all",
"layers",
"where",
"sampled",
"at",
"the",
"same",
"spatial",
"positions",
"for",
"each",
"image",
"allowi... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/aligned_activations.py#L35-L47 | [
"def",
"get_aligned_activations",
"(",
"layer",
")",
":",
"activation_paths",
"=",
"[",
"PATH_TEMPLATE",
".",
"format",
"(",
"sanitize",
"(",
"layer",
".",
"model_class",
".",
"name",
")",
",",
"sanitize",
"(",
"layer",
".",
"name",
")",
",",
"page",
")",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | layer_covariance | Computes the covariance matrix between the neurons of two layers. If only one
layer is passed, computes the symmetric covariance matrix of that layer. | lucid/modelzoo/aligned_activations.py | def layer_covariance(layer1, layer2=None):
"""Computes the covariance matrix between the neurons of two layers. If only one
layer is passed, computes the symmetric covariance matrix of that layer."""
layer2 = layer2 or layer1
act1, act2 = layer1.activations, layer2.activations
num_datapoints = act1.shape[0] # cast to avoid numpy type promotion during division
return np.matmul(act1.T, act2) / float(num_datapoints) | def layer_covariance(layer1, layer2=None):
"""Computes the covariance matrix between the neurons of two layers. If only one
layer is passed, computes the symmetric covariance matrix of that layer."""
layer2 = layer2 or layer1
act1, act2 = layer1.activations, layer2.activations
num_datapoints = act1.shape[0] # cast to avoid numpy type promotion during division
return np.matmul(act1.T, act2) / float(num_datapoints) | [
"Computes",
"the",
"covariance",
"matrix",
"between",
"the",
"neurons",
"of",
"two",
"layers",
".",
"If",
"only",
"one",
"layer",
"is",
"passed",
"computes",
"the",
"symmetric",
"covariance",
"matrix",
"of",
"that",
"layer",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/aligned_activations.py#L51-L57 | [
"def",
"layer_covariance",
"(",
"layer1",
",",
"layer2",
"=",
"None",
")",
":",
"layer2",
"=",
"layer2",
"or",
"layer1",
"act1",
",",
"act2",
"=",
"layer1",
".",
"activations",
",",
"layer2",
".",
"activations",
"num_datapoints",
"=",
"act1",
".",
"shape",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | push_activations | Push activations from one model to another using prerecorded correlations | lucid/modelzoo/aligned_activations.py | def push_activations(activations, from_layer, to_layer):
"""Push activations from one model to another using prerecorded correlations"""
inverse_covariance_matrix = layer_inverse_covariance(from_layer)
activations_decorrelated = np.dot(inverse_covariance_matrix, activations.T).T
covariance_matrix = layer_covariance(from_layer, to_layer)
activation_recorrelated = np.dot(activations_decorrelated, covariance_matrix)
return activation_recorrelated | def push_activations(activations, from_layer, to_layer):
"""Push activations from one model to another using prerecorded correlations"""
inverse_covariance_matrix = layer_inverse_covariance(from_layer)
activations_decorrelated = np.dot(inverse_covariance_matrix, activations.T).T
covariance_matrix = layer_covariance(from_layer, to_layer)
activation_recorrelated = np.dot(activations_decorrelated, covariance_matrix)
return activation_recorrelated | [
"Push",
"activations",
"from",
"one",
"model",
"to",
"another",
"using",
"prerecorded",
"correlations"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/aligned_activations.py#L66-L72 | [
"def",
"push_activations",
"(",
"activations",
",",
"from_layer",
",",
"to_layer",
")",
":",
"inverse_covariance_matrix",
"=",
"layer_inverse_covariance",
"(",
"from_layer",
")",
"activations_decorrelated",
"=",
"np",
".",
"dot",
"(",
"inverse_covariance_matrix",
",",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | multi_interpolation_basis | A paramaterization for interpolating between each pair of N objectives.
Sometimes you want to interpolate between optimizing a bunch of objectives,
in a paramaterization that encourages images to align.
Args:
n_objectives: number of objectives you want interpolate between
n_interp_steps: number of interpolation steps
width: width of intepolated images
channel
Returns:
A [n_objectives, n_objectives, n_interp_steps, width, width, channel]
shaped tensor, t, where the final [width, width, channel] should be
seen as images, such that the following properties hold:
t[a, b] = t[b, a, ::-1]
t[a, i, 0] = t[a, j, 0] for all i, j
t[a, a, i] = t[a, a, j] for all i, j
t[a, b, i] = t[b, a, -i] for all i | lucid/recipes/image_interpolation_params.py | def multi_interpolation_basis(n_objectives=6, n_interp_steps=5, width=128,
channels=3):
"""A paramaterization for interpolating between each pair of N objectives.
Sometimes you want to interpolate between optimizing a bunch of objectives,
in a paramaterization that encourages images to align.
Args:
n_objectives: number of objectives you want interpolate between
n_interp_steps: number of interpolation steps
width: width of intepolated images
channel
Returns:
A [n_objectives, n_objectives, n_interp_steps, width, width, channel]
shaped tensor, t, where the final [width, width, channel] should be
seen as images, such that the following properties hold:
t[a, b] = t[b, a, ::-1]
t[a, i, 0] = t[a, j, 0] for all i, j
t[a, a, i] = t[a, a, j] for all i, j
t[a, b, i] = t[b, a, -i] for all i
"""
N, M, W, Ch = n_objectives, n_interp_steps, width, channels
const_term = sum([lowres_tensor([W, W, Ch], [W//k, W//k, Ch])
for k in [1, 2, 4, 8]])
const_term = tf.reshape(const_term, [1, 1, 1, W, W, Ch])
example_interps = [
sum([lowres_tensor([M, W, W, Ch], [2, W//k, W//k, Ch])
for k in [1, 2, 4, 8]])
for _ in range(N)]
example_basis = []
for n in range(N):
col = []
for m in range(N):
interp = example_interps[n] + example_interps[m][::-1]
col.append(interp)
example_basis.append(col)
interp_basis = []
for n in range(N):
col = [interp_basis[m][N-n][::-1] for m in range(n)]
col.append(tf.zeros([M, W, W, 3]))
for m in range(n+1, N):
interp = sum([lowres_tensor([M, W, W, Ch], [M, W//k, W//k, Ch])
for k in [1, 2]])
col.append(interp)
interp_basis.append(col)
basis = []
for n in range(N):
col_ex = tf.stack(example_basis[n])
col_in = tf.stack(interp_basis[n])
basis.append(col_ex + col_in)
basis = tf.stack(basis)
return basis + const_term | def multi_interpolation_basis(n_objectives=6, n_interp_steps=5, width=128,
channels=3):
"""A paramaterization for interpolating between each pair of N objectives.
Sometimes you want to interpolate between optimizing a bunch of objectives,
in a paramaterization that encourages images to align.
Args:
n_objectives: number of objectives you want interpolate between
n_interp_steps: number of interpolation steps
width: width of intepolated images
channel
Returns:
A [n_objectives, n_objectives, n_interp_steps, width, width, channel]
shaped tensor, t, where the final [width, width, channel] should be
seen as images, such that the following properties hold:
t[a, b] = t[b, a, ::-1]
t[a, i, 0] = t[a, j, 0] for all i, j
t[a, a, i] = t[a, a, j] for all i, j
t[a, b, i] = t[b, a, -i] for all i
"""
N, M, W, Ch = n_objectives, n_interp_steps, width, channels
const_term = sum([lowres_tensor([W, W, Ch], [W//k, W//k, Ch])
for k in [1, 2, 4, 8]])
const_term = tf.reshape(const_term, [1, 1, 1, W, W, Ch])
example_interps = [
sum([lowres_tensor([M, W, W, Ch], [2, W//k, W//k, Ch])
for k in [1, 2, 4, 8]])
for _ in range(N)]
example_basis = []
for n in range(N):
col = []
for m in range(N):
interp = example_interps[n] + example_interps[m][::-1]
col.append(interp)
example_basis.append(col)
interp_basis = []
for n in range(N):
col = [interp_basis[m][N-n][::-1] for m in range(n)]
col.append(tf.zeros([M, W, W, 3]))
for m in range(n+1, N):
interp = sum([lowres_tensor([M, W, W, Ch], [M, W//k, W//k, Ch])
for k in [1, 2]])
col.append(interp)
interp_basis.append(col)
basis = []
for n in range(N):
col_ex = tf.stack(example_basis[n])
col_in = tf.stack(interp_basis[n])
basis.append(col_ex + col_in)
basis = tf.stack(basis)
return basis + const_term | [
"A",
"paramaterization",
"for",
"interpolating",
"between",
"each",
"pair",
"of",
"N",
"objectives",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/recipes/image_interpolation_params.py#L22-L82 | [
"def",
"multi_interpolation_basis",
"(",
"n_objectives",
"=",
"6",
",",
"n_interp_steps",
"=",
"5",
",",
"width",
"=",
"128",
",",
"channels",
"=",
"3",
")",
":",
"N",
",",
"M",
",",
"W",
",",
"Ch",
"=",
"n_objectives",
",",
"n_interp_steps",
",",
"wid... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | register_to_random_name | Register a gradient function to a random string.
In order to use a custom gradient in TensorFlow, it must be registered to a
string. This is both a hassle, and -- because only one function can every be
registered to a string -- annoying to iterate on in an interactive
environemnt.
This function registers a function to a unique random string of the form:
{FUNCTION_NAME}_{RANDOM_SALT}
And then returns the random string. This is a helper in creating more
convenient gradient overrides.
Args:
grad_f: gradient function to register. Should map (op, grad) -> grad(s)
Returns:
String that gradient function was registered to. | lucid/optvis/overrides/gradient_override.py | def register_to_random_name(grad_f):
"""Register a gradient function to a random string.
In order to use a custom gradient in TensorFlow, it must be registered to a
string. This is both a hassle, and -- because only one function can every be
registered to a string -- annoying to iterate on in an interactive
environemnt.
This function registers a function to a unique random string of the form:
{FUNCTION_NAME}_{RANDOM_SALT}
And then returns the random string. This is a helper in creating more
convenient gradient overrides.
Args:
grad_f: gradient function to register. Should map (op, grad) -> grad(s)
Returns:
String that gradient function was registered to.
"""
grad_f_name = grad_f.__name__ + "_" + str(uuid.uuid4())
tf.RegisterGradient(grad_f_name)(grad_f)
return grad_f_name | def register_to_random_name(grad_f):
"""Register a gradient function to a random string.
In order to use a custom gradient in TensorFlow, it must be registered to a
string. This is both a hassle, and -- because only one function can every be
registered to a string -- annoying to iterate on in an interactive
environemnt.
This function registers a function to a unique random string of the form:
{FUNCTION_NAME}_{RANDOM_SALT}
And then returns the random string. This is a helper in creating more
convenient gradient overrides.
Args:
grad_f: gradient function to register. Should map (op, grad) -> grad(s)
Returns:
String that gradient function was registered to.
"""
grad_f_name = grad_f.__name__ + "_" + str(uuid.uuid4())
tf.RegisterGradient(grad_f_name)(grad_f)
return grad_f_name | [
"Register",
"a",
"gradient",
"function",
"to",
"a",
"random",
"string",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/overrides/gradient_override.py#L50-L73 | [
"def",
"register_to_random_name",
"(",
"grad_f",
")",
":",
"grad_f_name",
"=",
"grad_f",
".",
"__name__",
"+",
"\"_\"",
"+",
"str",
"(",
"uuid",
".",
"uuid4",
"(",
")",
")",
"tf",
".",
"RegisterGradient",
"(",
"grad_f_name",
")",
"(",
"grad_f",
")",
"ret... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | gradient_override_map | Convenience wrapper for graph.gradient_override_map().
This functions provides two conveniences over normal tensorflow gradient
overrides: it auomatically uses the default graph instead of you needing to
find the graph, and it automatically
Example:
def _foo_grad_alt(op, grad): ...
with gradient_override({"Foo": _foo_grad_alt}):
Args:
override_dict: A dictionary describing how to override the gradient.
keys: strings correponding to the op type that should have their gradient
overriden.
values: functions or strings registered to gradient functions | lucid/optvis/overrides/gradient_override.py | def gradient_override_map(override_dict):
"""Convenience wrapper for graph.gradient_override_map().
This functions provides two conveniences over normal tensorflow gradient
overrides: it auomatically uses the default graph instead of you needing to
find the graph, and it automatically
Example:
def _foo_grad_alt(op, grad): ...
with gradient_override({"Foo": _foo_grad_alt}):
Args:
override_dict: A dictionary describing how to override the gradient.
keys: strings correponding to the op type that should have their gradient
overriden.
values: functions or strings registered to gradient functions
"""
override_dict_by_name = {}
for (op_name, grad_f) in override_dict.items():
if isinstance(grad_f, str):
override_dict_by_name[op_name] = grad_f
else:
override_dict_by_name[op_name] = register_to_random_name(grad_f)
with tf.get_default_graph().gradient_override_map(override_dict_by_name):
yield | def gradient_override_map(override_dict):
"""Convenience wrapper for graph.gradient_override_map().
This functions provides two conveniences over normal tensorflow gradient
overrides: it auomatically uses the default graph instead of you needing to
find the graph, and it automatically
Example:
def _foo_grad_alt(op, grad): ...
with gradient_override({"Foo": _foo_grad_alt}):
Args:
override_dict: A dictionary describing how to override the gradient.
keys: strings correponding to the op type that should have their gradient
overriden.
values: functions or strings registered to gradient functions
"""
override_dict_by_name = {}
for (op_name, grad_f) in override_dict.items():
if isinstance(grad_f, str):
override_dict_by_name[op_name] = grad_f
else:
override_dict_by_name[op_name] = register_to_random_name(grad_f)
with tf.get_default_graph().gradient_override_map(override_dict_by_name):
yield | [
"Convenience",
"wrapper",
"for",
"graph",
".",
"gradient_override_map",
"()",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/overrides/gradient_override.py#L77-L104 | [
"def",
"gradient_override_map",
"(",
"override_dict",
")",
":",
"override_dict_by_name",
"=",
"{",
"}",
"for",
"(",
"op_name",
",",
"grad_f",
")",
"in",
"override_dict",
".",
"items",
"(",
")",
":",
"if",
"isinstance",
"(",
"grad_f",
",",
"str",
")",
":",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | use_gradient | Decorator for easily setting custom gradients for TensorFlow functions.
* DO NOT use this function if you need to serialize your graph.
* This function will cause the decorated function to run slower.
Example:
def _foo_grad(op, grad): ...
@use_gradient(_foo_grad)
def foo(x1, x2, x3): ...
Args:
grad_f: function to use as gradient.
Returns:
A decorator to apply to the function you wish to override the gradient of. | lucid/optvis/overrides/gradient_override.py | def use_gradient(grad_f):
"""Decorator for easily setting custom gradients for TensorFlow functions.
* DO NOT use this function if you need to serialize your graph.
* This function will cause the decorated function to run slower.
Example:
def _foo_grad(op, grad): ...
@use_gradient(_foo_grad)
def foo(x1, x2, x3): ...
Args:
grad_f: function to use as gradient.
Returns:
A decorator to apply to the function you wish to override the gradient of.
"""
grad_f_name = register_to_random_name(grad_f)
def function_wrapper(f):
def inner(*inputs):
# TensorFlow only supports (as of writing) overriding the gradient of
# individual ops. In order to override the gardient of `f`, we need to
# somehow make it appear to be an individual TensorFlow op.
#
# Our solution is to create a PyFunc that mimics `f`.
#
# In particular, we construct a graph for `f` and run it, then use a
# stateful PyFunc to stash it's results in Python. Then we have another
# PyFunc mimic it by taking all the same inputs and returning the stashed
# output.
#
# I wish we could do this without PyFunc, but I don't see a way to have
# it be fully general.
state = {"out_value": None}
# First, we need to run `f` and store it's output.
out = f(*inputs)
def store_out(out_value):
"""Store the value of out to a python variable."""
state["out_value"] = out_value
store_name = "store_" + f.__name__
store = tf.py_func(store_out, [out], (), stateful=True, name=store_name)
# Next, we create the mock function, with an overriden gradient.
# Note that we need to make sure store gets evaluated before the mock
# runs.
def mock_f(*inputs):
"""Mimic f by retrieving the stored value of out."""
return state["out_value"]
with tf.control_dependencies([store]):
with gradient_override_map({"PyFunc": grad_f_name}):
mock_name = "mock_" + f.__name__
mock_out = tf.py_func(mock_f, inputs, out.dtype, stateful=True,
name=mock_name)
mock_out.set_shape(out.get_shape())
# Finally, we can return the mock.
return mock_out
return inner
return function_wrapper | def use_gradient(grad_f):
"""Decorator for easily setting custom gradients for TensorFlow functions.
* DO NOT use this function if you need to serialize your graph.
* This function will cause the decorated function to run slower.
Example:
def _foo_grad(op, grad): ...
@use_gradient(_foo_grad)
def foo(x1, x2, x3): ...
Args:
grad_f: function to use as gradient.
Returns:
A decorator to apply to the function you wish to override the gradient of.
"""
grad_f_name = register_to_random_name(grad_f)
def function_wrapper(f):
def inner(*inputs):
# TensorFlow only supports (as of writing) overriding the gradient of
# individual ops. In order to override the gardient of `f`, we need to
# somehow make it appear to be an individual TensorFlow op.
#
# Our solution is to create a PyFunc that mimics `f`.
#
# In particular, we construct a graph for `f` and run it, then use a
# stateful PyFunc to stash it's results in Python. Then we have another
# PyFunc mimic it by taking all the same inputs and returning the stashed
# output.
#
# I wish we could do this without PyFunc, but I don't see a way to have
# it be fully general.
state = {"out_value": None}
# First, we need to run `f` and store it's output.
out = f(*inputs)
def store_out(out_value):
"""Store the value of out to a python variable."""
state["out_value"] = out_value
store_name = "store_" + f.__name__
store = tf.py_func(store_out, [out], (), stateful=True, name=store_name)
# Next, we create the mock function, with an overriden gradient.
# Note that we need to make sure store gets evaluated before the mock
# runs.
def mock_f(*inputs):
"""Mimic f by retrieving the stored value of out."""
return state["out_value"]
with tf.control_dependencies([store]):
with gradient_override_map({"PyFunc": grad_f_name}):
mock_name = "mock_" + f.__name__
mock_out = tf.py_func(mock_f, inputs, out.dtype, stateful=True,
name=mock_name)
mock_out.set_shape(out.get_shape())
# Finally, we can return the mock.
return mock_out
return inner
return function_wrapper | [
"Decorator",
"for",
"easily",
"setting",
"custom",
"gradients",
"for",
"TensorFlow",
"functions",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/overrides/gradient_override.py#L107-L178 | [
"def",
"use_gradient",
"(",
"grad_f",
")",
":",
"grad_f_name",
"=",
"register_to_random_name",
"(",
"grad_f",
")",
"def",
"function_wrapper",
"(",
"f",
")",
":",
"def",
"inner",
"(",
"*",
"inputs",
")",
":",
"# TensorFlow only supports (as of writing) overriding the... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | pixel_image | A naive, pixel-based image parameterization.
Defaults to a random initialization, but can take a supplied init_val argument
instead.
Args:
shape: shape of resulting image, [batch, width, height, channels].
sd: standard deviation of param initialization noise.
init_val: an initial value to use instead of a random initialization. Needs
to have the same shape as the supplied shape argument.
Returns:
tensor with shape from first argument. | lucid/optvis/param/spatial.py | def pixel_image(shape, sd=None, init_val=None):
"""A naive, pixel-based image parameterization.
Defaults to a random initialization, but can take a supplied init_val argument
instead.
Args:
shape: shape of resulting image, [batch, width, height, channels].
sd: standard deviation of param initialization noise.
init_val: an initial value to use instead of a random initialization. Needs
to have the same shape as the supplied shape argument.
Returns:
tensor with shape from first argument.
"""
if sd is not None and init_val is not None:
warnings.warn(
"`pixel_image` received both an initial value and a sd argument. Ignoring sd in favor of the supplied initial value."
)
sd = sd or 0.01
init_val = init_val or np.random.normal(size=shape, scale=sd).astype(np.float32)
return tf.Variable(init_val) | def pixel_image(shape, sd=None, init_val=None):
"""A naive, pixel-based image parameterization.
Defaults to a random initialization, but can take a supplied init_val argument
instead.
Args:
shape: shape of resulting image, [batch, width, height, channels].
sd: standard deviation of param initialization noise.
init_val: an initial value to use instead of a random initialization. Needs
to have the same shape as the supplied shape argument.
Returns:
tensor with shape from first argument.
"""
if sd is not None and init_val is not None:
warnings.warn(
"`pixel_image` received both an initial value and a sd argument. Ignoring sd in favor of the supplied initial value."
)
sd = sd or 0.01
init_val = init_val or np.random.normal(size=shape, scale=sd).astype(np.float32)
return tf.Variable(init_val) | [
"A",
"naive",
"pixel",
"-",
"based",
"image",
"parameterization",
".",
"Defaults",
"to",
"a",
"random",
"initialization",
"but",
"can",
"take",
"a",
"supplied",
"init_val",
"argument",
"instead",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/spatial.py#L24-L45 | [
"def",
"pixel_image",
"(",
"shape",
",",
"sd",
"=",
"None",
",",
"init_val",
"=",
"None",
")",
":",
"if",
"sd",
"is",
"not",
"None",
"and",
"init_val",
"is",
"not",
"None",
":",
"warnings",
".",
"warn",
"(",
"\"`pixel_image` received both an initial value an... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | rfft2d_freqs | Computes 2D spectrum frequencies. | lucid/optvis/param/spatial.py | def rfft2d_freqs(h, w):
"""Computes 2D spectrum frequencies."""
fy = np.fft.fftfreq(h)[:, None]
# when we have an odd input dimension we need to keep one additional
# frequency and later cut off 1 pixel
if w % 2 == 1:
fx = np.fft.fftfreq(w)[: w // 2 + 2]
else:
fx = np.fft.fftfreq(w)[: w // 2 + 1]
return np.sqrt(fx * fx + fy * fy) | def rfft2d_freqs(h, w):
"""Computes 2D spectrum frequencies."""
fy = np.fft.fftfreq(h)[:, None]
# when we have an odd input dimension we need to keep one additional
# frequency and later cut off 1 pixel
if w % 2 == 1:
fx = np.fft.fftfreq(w)[: w // 2 + 2]
else:
fx = np.fft.fftfreq(w)[: w // 2 + 1]
return np.sqrt(fx * fx + fy * fy) | [
"Computes",
"2D",
"spectrum",
"frequencies",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/spatial.py#L48-L58 | [
"def",
"rfft2d_freqs",
"(",
"h",
",",
"w",
")",
":",
"fy",
"=",
"np",
".",
"fft",
".",
"fftfreq",
"(",
"h",
")",
"[",
":",
",",
"None",
"]",
"# when we have an odd input dimension we need to keep one additional",
"# frequency and later cut off 1 pixel",
"if",
"w",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | fft_image | An image paramaterization using 2D Fourier coefficients. | lucid/optvis/param/spatial.py | def fft_image(shape, sd=None, decay_power=1):
"""An image paramaterization using 2D Fourier coefficients."""
sd = sd or 0.01
batch, h, w, ch = shape
freqs = rfft2d_freqs(h, w)
init_val_size = (2, ch) + freqs.shape
images = []
for _ in range(batch):
# Create a random variable holding the actual 2D fourier coefficients
init_val = np.random.normal(size=init_val_size, scale=sd).astype(np.float32)
spectrum_real_imag_t = tf.Variable(init_val)
spectrum_t = tf.complex(spectrum_real_imag_t[0], spectrum_real_imag_t[1])
# Scale the spectrum. First normalize energy, then scale by the square-root
# of the number of pixels to get a unitary transformation.
# This allows to use similar leanring rates to pixel-wise optimisation.
scale = 1.0 / np.maximum(freqs, 1.0 / max(w, h)) ** decay_power
scale *= np.sqrt(w * h)
scaled_spectrum_t = scale * spectrum_t
# convert complex scaled spectrum to shape (h, w, ch) image tensor
# needs to transpose because irfft2d returns channels first
image_t = tf.transpose(tf.spectral.irfft2d(scaled_spectrum_t), (1, 2, 0))
# in case of odd spatial input dimensions we need to crop
image_t = image_t[:h, :w, :ch]
images.append(image_t)
batched_image_t = tf.stack(images) / 4.0 # TODO: is that a magic constant?
return batched_image_t | def fft_image(shape, sd=None, decay_power=1):
"""An image paramaterization using 2D Fourier coefficients."""
sd = sd or 0.01
batch, h, w, ch = shape
freqs = rfft2d_freqs(h, w)
init_val_size = (2, ch) + freqs.shape
images = []
for _ in range(batch):
# Create a random variable holding the actual 2D fourier coefficients
init_val = np.random.normal(size=init_val_size, scale=sd).astype(np.float32)
spectrum_real_imag_t = tf.Variable(init_val)
spectrum_t = tf.complex(spectrum_real_imag_t[0], spectrum_real_imag_t[1])
# Scale the spectrum. First normalize energy, then scale by the square-root
# of the number of pixels to get a unitary transformation.
# This allows to use similar leanring rates to pixel-wise optimisation.
scale = 1.0 / np.maximum(freqs, 1.0 / max(w, h)) ** decay_power
scale *= np.sqrt(w * h)
scaled_spectrum_t = scale * spectrum_t
# convert complex scaled spectrum to shape (h, w, ch) image tensor
# needs to transpose because irfft2d returns channels first
image_t = tf.transpose(tf.spectral.irfft2d(scaled_spectrum_t), (1, 2, 0))
# in case of odd spatial input dimensions we need to crop
image_t = image_t[:h, :w, :ch]
images.append(image_t)
batched_image_t = tf.stack(images) / 4.0 # TODO: is that a magic constant?
return batched_image_t | [
"An",
"image",
"paramaterization",
"using",
"2D",
"Fourier",
"coefficients",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/spatial.py#L61-L93 | [
"def",
"fft_image",
"(",
"shape",
",",
"sd",
"=",
"None",
",",
"decay_power",
"=",
"1",
")",
":",
"sd",
"=",
"sd",
"or",
"0.01",
"batch",
",",
"h",
",",
"w",
",",
"ch",
"=",
"shape",
"freqs",
"=",
"rfft2d_freqs",
"(",
"h",
",",
"w",
")",
"init_... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | laplacian_pyramid_image | Simple laplacian pyramid paramaterization of an image.
For more flexibility, use a sum of lowres_tensor()s.
Args:
shape: shape of resulting image, [batch, width, height, channels].
n_levels: number of levels of laplacian pyarmid.
sd: standard deviation of param initialization.
Returns:
tensor with shape from first argument. | lucid/optvis/param/spatial.py | def laplacian_pyramid_image(shape, n_levels=4, sd=None):
"""Simple laplacian pyramid paramaterization of an image.
For more flexibility, use a sum of lowres_tensor()s.
Args:
shape: shape of resulting image, [batch, width, height, channels].
n_levels: number of levels of laplacian pyarmid.
sd: standard deviation of param initialization.
Returns:
tensor with shape from first argument.
"""
batch_dims = shape[:-3]
w, h, ch = shape[-3:]
pyramid = 0
for n in range(n_levels):
k = 2 ** n
pyramid += lowres_tensor(shape, batch_dims + (w // k, h // k, ch), sd=sd)
return pyramid | def laplacian_pyramid_image(shape, n_levels=4, sd=None):
"""Simple laplacian pyramid paramaterization of an image.
For more flexibility, use a sum of lowres_tensor()s.
Args:
shape: shape of resulting image, [batch, width, height, channels].
n_levels: number of levels of laplacian pyarmid.
sd: standard deviation of param initialization.
Returns:
tensor with shape from first argument.
"""
batch_dims = shape[:-3]
w, h, ch = shape[-3:]
pyramid = 0
for n in range(n_levels):
k = 2 ** n
pyramid += lowres_tensor(shape, batch_dims + (w // k, h // k, ch), sd=sd)
return pyramid | [
"Simple",
"laplacian",
"pyramid",
"paramaterization",
"of",
"an",
"image",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/spatial.py#L96-L115 | [
"def",
"laplacian_pyramid_image",
"(",
"shape",
",",
"n_levels",
"=",
"4",
",",
"sd",
"=",
"None",
")",
":",
"batch_dims",
"=",
"shape",
"[",
":",
"-",
"3",
"]",
"w",
",",
"h",
",",
"ch",
"=",
"shape",
"[",
"-",
"3",
":",
"]",
"pyramid",
"=",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | bilinearly_sampled_image | Build bilinear texture sampling graph.
Coordinate transformation rules match OpenGL GL_REPEAT wrapping and GL_LINEAR
interpolation modes.
Args:
texture: [tex_h, tex_w, channel_n] tensor.
uv: [frame_h, frame_h, 2] tensor with per-pixel UV coordinates in range [0..1]
Returns:
[frame_h, frame_h, channel_n] tensor with per-pixel sampled values. | lucid/optvis/param/spatial.py | def bilinearly_sampled_image(texture, uv):
"""Build bilinear texture sampling graph.
Coordinate transformation rules match OpenGL GL_REPEAT wrapping and GL_LINEAR
interpolation modes.
Args:
texture: [tex_h, tex_w, channel_n] tensor.
uv: [frame_h, frame_h, 2] tensor with per-pixel UV coordinates in range [0..1]
Returns:
[frame_h, frame_h, channel_n] tensor with per-pixel sampled values.
"""
h, w = tf.unstack(tf.shape(texture)[:2])
u, v = tf.split(uv, 2, axis=-1)
v = 1.0 - v # vertical flip to match GL convention
u, v = u * tf.to_float(w) - 0.5, v * tf.to_float(h) - 0.5
u0, u1 = tf.floor(u), tf.ceil(u)
v0, v1 = tf.floor(v), tf.ceil(v)
uf, vf = u - u0, v - v0
u0, u1, v0, v1 = map(tf.to_int32, [u0, u1, v0, v1])
def sample(u, v):
vu = tf.concat([v % h, u % w], axis=-1)
return tf.gather_nd(texture, vu)
s00, s01 = sample(u0, v0), sample(u0, v1)
s10, s11 = sample(u1, v0), sample(u1, v1)
s0 = s00 * (1.0 - vf) + s01 * vf
s1 = s10 * (1.0 - vf) + s11 * vf
s = s0 * (1.0 - uf) + s1 * uf
return s | def bilinearly_sampled_image(texture, uv):
"""Build bilinear texture sampling graph.
Coordinate transformation rules match OpenGL GL_REPEAT wrapping and GL_LINEAR
interpolation modes.
Args:
texture: [tex_h, tex_w, channel_n] tensor.
uv: [frame_h, frame_h, 2] tensor with per-pixel UV coordinates in range [0..1]
Returns:
[frame_h, frame_h, channel_n] tensor with per-pixel sampled values.
"""
h, w = tf.unstack(tf.shape(texture)[:2])
u, v = tf.split(uv, 2, axis=-1)
v = 1.0 - v # vertical flip to match GL convention
u, v = u * tf.to_float(w) - 0.5, v * tf.to_float(h) - 0.5
u0, u1 = tf.floor(u), tf.ceil(u)
v0, v1 = tf.floor(v), tf.ceil(v)
uf, vf = u - u0, v - v0
u0, u1, v0, v1 = map(tf.to_int32, [u0, u1, v0, v1])
def sample(u, v):
vu = tf.concat([v % h, u % w], axis=-1)
return tf.gather_nd(texture, vu)
s00, s01 = sample(u0, v0), sample(u0, v1)
s10, s11 = sample(u1, v0), sample(u1, v1)
s0 = s00 * (1.0 - vf) + s01 * vf
s1 = s10 * (1.0 - vf) + s11 * vf
s = s0 * (1.0 - uf) + s1 * uf
return s | [
"Build",
"bilinear",
"texture",
"sampling",
"graph",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/spatial.py#L118-L149 | [
"def",
"bilinearly_sampled_image",
"(",
"texture",
",",
"uv",
")",
":",
"h",
",",
"w",
"=",
"tf",
".",
"unstack",
"(",
"tf",
".",
"shape",
"(",
"texture",
")",
"[",
":",
"2",
"]",
")",
"u",
",",
"v",
"=",
"tf",
".",
"split",
"(",
"uv",
",",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _linear_decorelate_color | Multiply input by sqrt of emperical (ImageNet) color correlation matrix.
If you interpret t's innermost dimension as describing colors in a
decorrelated version of the color space (which is a very natural way to
describe colors -- see discussion in Feature Visualization article) the way
to map back to normal colors is multiply the square root of your color
correlations. | lucid/optvis/param/color.py | def _linear_decorelate_color(t):
"""Multiply input by sqrt of emperical (ImageNet) color correlation matrix.
If you interpret t's innermost dimension as describing colors in a
decorrelated version of the color space (which is a very natural way to
describe colors -- see discussion in Feature Visualization article) the way
to map back to normal colors is multiply the square root of your color
correlations.
"""
# check that inner dimension is 3?
t_flat = tf.reshape(t, [-1, 3])
color_correlation_normalized = color_correlation_svd_sqrt / max_norm_svd_sqrt
t_flat = tf.matmul(t_flat, color_correlation_normalized.T)
t = tf.reshape(t_flat, tf.shape(t))
return t | def _linear_decorelate_color(t):
"""Multiply input by sqrt of emperical (ImageNet) color correlation matrix.
If you interpret t's innermost dimension as describing colors in a
decorrelated version of the color space (which is a very natural way to
describe colors -- see discussion in Feature Visualization article) the way
to map back to normal colors is multiply the square root of your color
correlations.
"""
# check that inner dimension is 3?
t_flat = tf.reshape(t, [-1, 3])
color_correlation_normalized = color_correlation_svd_sqrt / max_norm_svd_sqrt
t_flat = tf.matmul(t_flat, color_correlation_normalized.T)
t = tf.reshape(t_flat, tf.shape(t))
return t | [
"Multiply",
"input",
"by",
"sqrt",
"of",
"emperical",
"(",
"ImageNet",
")",
"color",
"correlation",
"matrix",
".",
"If",
"you",
"interpret",
"t",
"s",
"innermost",
"dimension",
"as",
"describing",
"colors",
"in",
"a",
"decorrelated",
"version",
"of",
"the",
... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/color.py#L32-L46 | [
"def",
"_linear_decorelate_color",
"(",
"t",
")",
":",
"# check that inner dimension is 3?",
"t_flat",
"=",
"tf",
".",
"reshape",
"(",
"t",
",",
"[",
"-",
"1",
",",
"3",
"]",
")",
"color_correlation_normalized",
"=",
"color_correlation_svd_sqrt",
"/",
"max_norm_sv... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | to_valid_rgb | Transform inner dimension of t to valid rgb colors.
In practice this consistes of two parts:
(1) If requested, transform the colors from a decorrelated color space to RGB.
(2) Constrain the color channels to be in [0,1], either using a sigmoid
function or clipping.
Args:
t: input tensor, innermost dimension will be interpreted as colors
and transformed/constrained.
decorrelate: should the input tensor's colors be interpreted as coming from
a whitened space or not?
sigmoid: should the colors be constrained using sigmoid (if True) or
clipping (if False).
Returns:
t with the innermost dimension transformed. | lucid/optvis/param/color.py | def to_valid_rgb(t, decorrelate=False, sigmoid=True):
"""Transform inner dimension of t to valid rgb colors.
In practice this consistes of two parts:
(1) If requested, transform the colors from a decorrelated color space to RGB.
(2) Constrain the color channels to be in [0,1], either using a sigmoid
function or clipping.
Args:
t: input tensor, innermost dimension will be interpreted as colors
and transformed/constrained.
decorrelate: should the input tensor's colors be interpreted as coming from
a whitened space or not?
sigmoid: should the colors be constrained using sigmoid (if True) or
clipping (if False).
Returns:
t with the innermost dimension transformed.
"""
if decorrelate:
t = _linear_decorelate_color(t)
if decorrelate and not sigmoid:
t += color_mean
if sigmoid:
return tf.nn.sigmoid(t)
else:
return constrain_L_inf(2*t-1)/2 + 0.5 | def to_valid_rgb(t, decorrelate=False, sigmoid=True):
"""Transform inner dimension of t to valid rgb colors.
In practice this consistes of two parts:
(1) If requested, transform the colors from a decorrelated color space to RGB.
(2) Constrain the color channels to be in [0,1], either using a sigmoid
function or clipping.
Args:
t: input tensor, innermost dimension will be interpreted as colors
and transformed/constrained.
decorrelate: should the input tensor's colors be interpreted as coming from
a whitened space or not?
sigmoid: should the colors be constrained using sigmoid (if True) or
clipping (if False).
Returns:
t with the innermost dimension transformed.
"""
if decorrelate:
t = _linear_decorelate_color(t)
if decorrelate and not sigmoid:
t += color_mean
if sigmoid:
return tf.nn.sigmoid(t)
else:
return constrain_L_inf(2*t-1)/2 + 0.5 | [
"Transform",
"inner",
"dimension",
"of",
"t",
"to",
"valid",
"rgb",
"colors",
".",
"In",
"practice",
"this",
"consistes",
"of",
"two",
"parts",
":",
"(",
"1",
")",
"If",
"requested",
"transform",
"the",
"colors",
"from",
"a",
"decorrelated",
"color",
"spac... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/color.py#L49-L75 | [
"def",
"to_valid_rgb",
"(",
"t",
",",
"decorrelate",
"=",
"False",
",",
"sigmoid",
"=",
"True",
")",
":",
"if",
"decorrelate",
":",
"t",
"=",
"_linear_decorelate_color",
"(",
"t",
")",
"if",
"decorrelate",
"and",
"not",
"sigmoid",
":",
"t",
"+=",
"color_... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _populate_inception_bottlenecks | Add Inception bottlenecks and their pre-Relu versions to the graph. | lucid/modelzoo/other_models/InceptionV1.py | def _populate_inception_bottlenecks(scope):
"""Add Inception bottlenecks and their pre-Relu versions to the graph."""
graph = tf.get_default_graph()
for op in graph.get_operations():
if op.name.startswith(scope+'/') and 'Concat' in op.type:
name = op.name.split('/')[1]
pre_relus = []
for tower in op.inputs[1:]:
if tower.op.type == 'Relu':
tower = tower.op.inputs[0]
pre_relus.append(tower)
concat_name = scope + '/' + name + '_pre_relu'
_ = tf.concat(pre_relus, -1, name=concat_name) | def _populate_inception_bottlenecks(scope):
"""Add Inception bottlenecks and their pre-Relu versions to the graph."""
graph = tf.get_default_graph()
for op in graph.get_operations():
if op.name.startswith(scope+'/') and 'Concat' in op.type:
name = op.name.split('/')[1]
pre_relus = []
for tower in op.inputs[1:]:
if tower.op.type == 'Relu':
tower = tower.op.inputs[0]
pre_relus.append(tower)
concat_name = scope + '/' + name + '_pre_relu'
_ = tf.concat(pre_relus, -1, name=concat_name) | [
"Add",
"Inception",
"bottlenecks",
"and",
"their",
"pre",
"-",
"Relu",
"versions",
"to",
"the",
"graph",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/modelzoo/other_models/InceptionV1.py#L22-L34 | [
"def",
"_populate_inception_bottlenecks",
"(",
"scope",
")",
":",
"graph",
"=",
"tf",
".",
"get_default_graph",
"(",
")",
"for",
"op",
"in",
"graph",
".",
"get_operations",
"(",
")",
":",
"if",
"op",
".",
"name",
".",
"startswith",
"(",
"scope",
"+",
"'/... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | wrap_objective | Decorator for creating Objective factories.
Changes f from the closure: (args) => () => TF Tensor
into an Obejective factory: (args) => Objective
while perserving function name, arg info, docs... for interactive python. | lucid/optvis/objectives.py | def wrap_objective(f, *args, **kwds):
"""Decorator for creating Objective factories.
Changes f from the closure: (args) => () => TF Tensor
into an Obejective factory: (args) => Objective
while perserving function name, arg info, docs... for interactive python.
"""
objective_func = f(*args, **kwds)
objective_name = f.__name__
args_str = " [" + ", ".join([_make_arg_str(arg) for arg in args]) + "]"
description = objective_name.title() + args_str
return Objective(objective_func, objective_name, description) | def wrap_objective(f, *args, **kwds):
"""Decorator for creating Objective factories.
Changes f from the closure: (args) => () => TF Tensor
into an Obejective factory: (args) => Objective
while perserving function name, arg info, docs... for interactive python.
"""
objective_func = f(*args, **kwds)
objective_name = f.__name__
args_str = " [" + ", ".join([_make_arg_str(arg) for arg in args]) + "]"
description = objective_name.title() + args_str
return Objective(objective_func, objective_name, description) | [
"Decorator",
"for",
"creating",
"Objective",
"factories",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L117-L129 | [
"def",
"wrap_objective",
"(",
"f",
",",
"*",
"args",
",",
"*",
"*",
"kwds",
")",
":",
"objective_func",
"=",
"f",
"(",
"*",
"args",
",",
"*",
"*",
"kwds",
")",
"objective_name",
"=",
"f",
".",
"__name__",
"args_str",
"=",
"\" [\"",
"+",
"\", \"",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | neuron | Visualize a single neuron of a single channel.
Defaults to the center neuron. When width and height are even numbers, we
choose the neuron in the bottom right of the center 2x2 neurons.
Odd width & height: Even width & height:
+---+---+---+ +---+---+---+---+
| | | | | | | | |
+---+---+---+ +---+---+---+---+
| | X | | | | | | |
+---+---+---+ +---+---+---+---+
| | | | | | | X | |
+---+---+---+ +---+---+---+---+
| | | | |
+---+---+---+---+ | lucid/optvis/objectives.py | def neuron(layer_name, channel_n, x=None, y=None, batch=None):
"""Visualize a single neuron of a single channel.
Defaults to the center neuron. When width and height are even numbers, we
choose the neuron in the bottom right of the center 2x2 neurons.
Odd width & height: Even width & height:
+---+---+---+ +---+---+---+---+
| | | | | | | | |
+---+---+---+ +---+---+---+---+
| | X | | | | | | |
+---+---+---+ +---+---+---+---+
| | | | | | | X | |
+---+---+---+ +---+---+---+---+
| | | | |
+---+---+---+---+
"""
def inner(T):
layer = T(layer_name)
shape = tf.shape(layer)
x_ = shape[1] // 2 if x is None else x
y_ = shape[2] // 2 if y is None else y
if batch is None:
return layer[:, x_, y_, channel_n]
else:
return layer[batch, x_, y_, channel_n]
return inner | def neuron(layer_name, channel_n, x=None, y=None, batch=None):
"""Visualize a single neuron of a single channel.
Defaults to the center neuron. When width and height are even numbers, we
choose the neuron in the bottom right of the center 2x2 neurons.
Odd width & height: Even width & height:
+---+---+---+ +---+---+---+---+
| | | | | | | | |
+---+---+---+ +---+---+---+---+
| | X | | | | | | |
+---+---+---+ +---+---+---+---+
| | | | | | | X | |
+---+---+---+ +---+---+---+---+
| | | | |
+---+---+---+---+
"""
def inner(T):
layer = T(layer_name)
shape = tf.shape(layer)
x_ = shape[1] // 2 if x is None else x
y_ = shape[2] // 2 if y is None else y
if batch is None:
return layer[:, x_, y_, channel_n]
else:
return layer[batch, x_, y_, channel_n]
return inner | [
"Visualize",
"a",
"single",
"neuron",
"of",
"a",
"single",
"channel",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L133-L161 | [
"def",
"neuron",
"(",
"layer_name",
",",
"channel_n",
",",
"x",
"=",
"None",
",",
"y",
"=",
"None",
",",
"batch",
"=",
"None",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"layer",
"=",
"T",
"(",
"layer_name",
")",
"shape",
"=",
"tf",
".",
"sh... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | channel | Visualize a single channel | lucid/optvis/objectives.py | def channel(layer, n_channel, batch=None):
"""Visualize a single channel"""
if batch is None:
return lambda T: tf.reduce_mean(T(layer)[..., n_channel])
else:
return lambda T: tf.reduce_mean(T(layer)[batch, ..., n_channel]) | def channel(layer, n_channel, batch=None):
"""Visualize a single channel"""
if batch is None:
return lambda T: tf.reduce_mean(T(layer)[..., n_channel])
else:
return lambda T: tf.reduce_mean(T(layer)[batch, ..., n_channel]) | [
"Visualize",
"a",
"single",
"channel"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L165-L170 | [
"def",
"channel",
"(",
"layer",
",",
"n_channel",
",",
"batch",
"=",
"None",
")",
":",
"if",
"batch",
"is",
"None",
":",
"return",
"lambda",
"T",
":",
"tf",
".",
"reduce_mean",
"(",
"T",
"(",
"layer",
")",
"[",
"...",
",",
"n_channel",
"]",
")",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | direction | Visualize a direction | lucid/optvis/objectives.py | def direction(layer, vec, batch=None, cossim_pow=0):
"""Visualize a direction"""
if batch is None:
vec = vec[None, None, None]
return lambda T: _dot_cossim(T(layer), vec)
else:
vec = vec[None, None]
return lambda T: _dot_cossim(T(layer)[batch], vec) | def direction(layer, vec, batch=None, cossim_pow=0):
"""Visualize a direction"""
if batch is None:
vec = vec[None, None, None]
return lambda T: _dot_cossim(T(layer), vec)
else:
vec = vec[None, None]
return lambda T: _dot_cossim(T(layer)[batch], vec) | [
"Visualize",
"a",
"direction"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L189-L196 | [
"def",
"direction",
"(",
"layer",
",",
"vec",
",",
"batch",
"=",
"None",
",",
"cossim_pow",
"=",
"0",
")",
":",
"if",
"batch",
"is",
"None",
":",
"vec",
"=",
"vec",
"[",
"None",
",",
"None",
",",
"None",
"]",
"return",
"lambda",
"T",
":",
"_dot_c... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | direction_neuron | Visualize a single (x, y) position along the given direction | lucid/optvis/objectives.py | def direction_neuron(layer_name, vec, batch=None, x=None, y=None, cossim_pow=0):
"""Visualize a single (x, y) position along the given direction"""
def inner(T):
layer = T(layer_name)
shape = tf.shape(layer)
x_ = shape[1] // 2 if x is None else x
y_ = shape[2] // 2 if y is None else y
if batch is None:
return _dot_cossim(layer[:, x_, y_], vec[None], cossim_pow=cossim_pow)
else:
return _dot_cossim(layer[batch, x_, y_], vec, cossim_pow=cossim_pow)
return inner | def direction_neuron(layer_name, vec, batch=None, x=None, y=None, cossim_pow=0):
"""Visualize a single (x, y) position along the given direction"""
def inner(T):
layer = T(layer_name)
shape = tf.shape(layer)
x_ = shape[1] // 2 if x is None else x
y_ = shape[2] // 2 if y is None else y
if batch is None:
return _dot_cossim(layer[:, x_, y_], vec[None], cossim_pow=cossim_pow)
else:
return _dot_cossim(layer[batch, x_, y_], vec, cossim_pow=cossim_pow)
return inner | [
"Visualize",
"a",
"single",
"(",
"x",
"y",
")",
"position",
"along",
"the",
"given",
"direction"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L200-L211 | [
"def",
"direction_neuron",
"(",
"layer_name",
",",
"vec",
",",
"batch",
"=",
"None",
",",
"x",
"=",
"None",
",",
"y",
"=",
"None",
",",
"cossim_pow",
"=",
"0",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"layer",
"=",
"T",
"(",
"layer_name",
")... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | direction_cossim | Visualize a direction (cossine similarity) | lucid/optvis/objectives.py | def direction_cossim(layer, vec, batch=None):
"""Visualize a direction (cossine similarity)"""
def inner(T):
act_mags = tf.sqrt(tf.reduce_sum(T(layer)**2, -1, keepdims=True))
vec_mag = tf.sqrt(tf.reduce_sum(vec**2))
mags = act_mags * vec_mag
if batch is None:
return tf.reduce_mean(T(layer) * vec.reshape([1, 1, 1, -1]) / mags)
else:
return tf.reduce_mean(T(layer)[batch] * vec.reshape([1, 1, -1]) / mags)
return inner | def direction_cossim(layer, vec, batch=None):
"""Visualize a direction (cossine similarity)"""
def inner(T):
act_mags = tf.sqrt(tf.reduce_sum(T(layer)**2, -1, keepdims=True))
vec_mag = tf.sqrt(tf.reduce_sum(vec**2))
mags = act_mags * vec_mag
if batch is None:
return tf.reduce_mean(T(layer) * vec.reshape([1, 1, 1, -1]) / mags)
else:
return tf.reduce_mean(T(layer)[batch] * vec.reshape([1, 1, -1]) / mags)
return inner | [
"Visualize",
"a",
"direction",
"(",
"cossine",
"similarity",
")"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L214-L224 | [
"def",
"direction_cossim",
"(",
"layer",
",",
"vec",
",",
"batch",
"=",
"None",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"act_mags",
"=",
"tf",
".",
"sqrt",
"(",
"tf",
".",
"reduce_sum",
"(",
"T",
"(",
"layer",
")",
"**",
"2",
",",
"-",
"1... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | L1 | L1 norm of layer. Generally used as penalty. | lucid/optvis/objectives.py | def L1(layer="input", constant=0, batch=None):
"""L1 norm of layer. Generally used as penalty."""
if batch is None:
return lambda T: tf.reduce_sum(tf.abs(T(layer) - constant))
else:
return lambda T: tf.reduce_sum(tf.abs(T(layer)[batch] - constant)) | def L1(layer="input", constant=0, batch=None):
"""L1 norm of layer. Generally used as penalty."""
if batch is None:
return lambda T: tf.reduce_sum(tf.abs(T(layer) - constant))
else:
return lambda T: tf.reduce_sum(tf.abs(T(layer)[batch] - constant)) | [
"L1",
"norm",
"of",
"layer",
".",
"Generally",
"used",
"as",
"penalty",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L247-L252 | [
"def",
"L1",
"(",
"layer",
"=",
"\"input\"",
",",
"constant",
"=",
"0",
",",
"batch",
"=",
"None",
")",
":",
"if",
"batch",
"is",
"None",
":",
"return",
"lambda",
"T",
":",
"tf",
".",
"reduce_sum",
"(",
"tf",
".",
"abs",
"(",
"T",
"(",
"layer",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | L2 | L2 norm of layer. Generally used as penalty. | lucid/optvis/objectives.py | def L2(layer="input", constant=0, epsilon=1e-6, batch=None):
"""L2 norm of layer. Generally used as penalty."""
if batch is None:
return lambda T: tf.sqrt(epsilon + tf.reduce_sum((T(layer) - constant) ** 2))
else:
return lambda T: tf.sqrt(epsilon + tf.reduce_sum((T(layer)[batch] - constant) ** 2)) | def L2(layer="input", constant=0, epsilon=1e-6, batch=None):
"""L2 norm of layer. Generally used as penalty."""
if batch is None:
return lambda T: tf.sqrt(epsilon + tf.reduce_sum((T(layer) - constant) ** 2))
else:
return lambda T: tf.sqrt(epsilon + tf.reduce_sum((T(layer)[batch] - constant) ** 2)) | [
"L2",
"norm",
"of",
"layer",
".",
"Generally",
"used",
"as",
"penalty",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L256-L261 | [
"def",
"L2",
"(",
"layer",
"=",
"\"input\"",
",",
"constant",
"=",
"0",
",",
"epsilon",
"=",
"1e-6",
",",
"batch",
"=",
"None",
")",
":",
"if",
"batch",
"is",
"None",
":",
"return",
"lambda",
"T",
":",
"tf",
".",
"sqrt",
"(",
"epsilon",
"+",
"tf"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | blur_input_each_step | Minimizing this objective is equivelant to blurring input each step.
Optimizing (-k)*blur_input_each_step() is equivelant to:
input <- (1-k)*input + k*blur(input)
An operation that was used in early feature visualization work.
See Nguyen, et al., 2015. | lucid/optvis/objectives.py | def blur_input_each_step():
"""Minimizing this objective is equivelant to blurring input each step.
Optimizing (-k)*blur_input_each_step() is equivelant to:
input <- (1-k)*input + k*blur(input)
An operation that was used in early feature visualization work.
See Nguyen, et al., 2015.
"""
def inner(T):
t_input = T("input")
t_input_blurred = tf.stop_gradient(_tf_blur(t_input))
return 0.5*tf.reduce_sum((t_input - t_input_blurred)**2)
return inner | def blur_input_each_step():
"""Minimizing this objective is equivelant to blurring input each step.
Optimizing (-k)*blur_input_each_step() is equivelant to:
input <- (1-k)*input + k*blur(input)
An operation that was used in early feature visualization work.
See Nguyen, et al., 2015.
"""
def inner(T):
t_input = T("input")
t_input_blurred = tf.stop_gradient(_tf_blur(t_input))
return 0.5*tf.reduce_sum((t_input - t_input_blurred)**2)
return inner | [
"Minimizing",
"this",
"objective",
"is",
"equivelant",
"to",
"blurring",
"input",
"each",
"step",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L277-L291 | [
"def",
"blur_input_each_step",
"(",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"t_input",
"=",
"T",
"(",
"\"input\"",
")",
"t_input_blurred",
"=",
"tf",
".",
"stop_gradient",
"(",
"_tf_blur",
"(",
"t_input",
")",
")",
"return",
"0.5",
"*",
"tf",
"."... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | channel_interpolate | Interpolate between layer1, n_channel1 and layer2, n_channel2.
Optimize for a convex combination of layer1, n_channel1 and
layer2, n_channel2, transitioning across the batch.
Args:
layer1: layer to optimize 100% at batch=0.
n_channel1: neuron index to optimize 100% at batch=0.
layer2: layer to optimize 100% at batch=N.
n_channel2: neuron index to optimize 100% at batch=N.
Returns:
Objective | lucid/optvis/objectives.py | def channel_interpolate(layer1, n_channel1, layer2, n_channel2):
"""Interpolate between layer1, n_channel1 and layer2, n_channel2.
Optimize for a convex combination of layer1, n_channel1 and
layer2, n_channel2, transitioning across the batch.
Args:
layer1: layer to optimize 100% at batch=0.
n_channel1: neuron index to optimize 100% at batch=0.
layer2: layer to optimize 100% at batch=N.
n_channel2: neuron index to optimize 100% at batch=N.
Returns:
Objective
"""
def inner(T):
batch_n = T(layer1).get_shape().as_list()[0]
arr1 = T(layer1)[..., n_channel1]
arr2 = T(layer2)[..., n_channel2]
weights = (np.arange(batch_n)/float(batch_n-1))
S = 0
for n in range(batch_n):
S += (1-weights[n]) * tf.reduce_mean(arr1[n])
S += weights[n] * tf.reduce_mean(arr2[n])
return S
return inner | def channel_interpolate(layer1, n_channel1, layer2, n_channel2):
"""Interpolate between layer1, n_channel1 and layer2, n_channel2.
Optimize for a convex combination of layer1, n_channel1 and
layer2, n_channel2, transitioning across the batch.
Args:
layer1: layer to optimize 100% at batch=0.
n_channel1: neuron index to optimize 100% at batch=0.
layer2: layer to optimize 100% at batch=N.
n_channel2: neuron index to optimize 100% at batch=N.
Returns:
Objective
"""
def inner(T):
batch_n = T(layer1).get_shape().as_list()[0]
arr1 = T(layer1)[..., n_channel1]
arr2 = T(layer2)[..., n_channel2]
weights = (np.arange(batch_n)/float(batch_n-1))
S = 0
for n in range(batch_n):
S += (1-weights[n]) * tf.reduce_mean(arr1[n])
S += weights[n] * tf.reduce_mean(arr2[n])
return S
return inner | [
"Interpolate",
"between",
"layer1",
"n_channel1",
"and",
"layer2",
"n_channel2",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L303-L328 | [
"def",
"channel_interpolate",
"(",
"layer1",
",",
"n_channel1",
",",
"layer2",
",",
"n_channel2",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"batch_n",
"=",
"T",
"(",
"layer1",
")",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"[",
"0",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | penalize_boundary_complexity | Encourage the boundaries of an image to have less variation and of color C.
Args:
shp: shape of T("input") because this may not be known.
w: width of boundary to penalize. Ignored if mask is set.
mask: mask describing what area should be penalized.
Returns:
Objective. | lucid/optvis/objectives.py | def penalize_boundary_complexity(shp, w=20, mask=None, C=0.5):
"""Encourage the boundaries of an image to have less variation and of color C.
Args:
shp: shape of T("input") because this may not be known.
w: width of boundary to penalize. Ignored if mask is set.
mask: mask describing what area should be penalized.
Returns:
Objective.
"""
def inner(T):
arr = T("input")
# print shp
if mask is None:
mask_ = np.ones(shp)
mask_[:, w:-w, w:-w] = 0
else:
mask_ = mask
blur = _tf_blur(arr, w=5)
diffs = (blur-arr)**2
diffs += 0.8*(arr-C)**2
return -tf.reduce_sum(diffs*mask_)
return inner | def penalize_boundary_complexity(shp, w=20, mask=None, C=0.5):
"""Encourage the boundaries of an image to have less variation and of color C.
Args:
shp: shape of T("input") because this may not be known.
w: width of boundary to penalize. Ignored if mask is set.
mask: mask describing what area should be penalized.
Returns:
Objective.
"""
def inner(T):
arr = T("input")
# print shp
if mask is None:
mask_ = np.ones(shp)
mask_[:, w:-w, w:-w] = 0
else:
mask_ = mask
blur = _tf_blur(arr, w=5)
diffs = (blur-arr)**2
diffs += 0.8*(arr-C)**2
return -tf.reduce_sum(diffs*mask_)
return inner | [
"Encourage",
"the",
"boundaries",
"of",
"an",
"image",
"to",
"have",
"less",
"variation",
"and",
"of",
"color",
"C",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L332-L358 | [
"def",
"penalize_boundary_complexity",
"(",
"shp",
",",
"w",
"=",
"20",
",",
"mask",
"=",
"None",
",",
"C",
"=",
"0.5",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"arr",
"=",
"T",
"(",
"\"input\"",
")",
"# print shp",
"if",
"mask",
"is",
"None",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | alignment | Encourage neighboring images to be similar.
When visualizing the interpolation between two objectives, it's often
desireable to encourage analagous boejcts to be drawn in the same position,
to make them more comparable.
This term penalizes L2 distance between neighboring images, as evaluated at
layer.
In general, we find this most effective if used with a paramaterization that
shares across the batch. (In fact, that works quite well by iteself, so this
function may just be obselete.)
Args:
layer: layer to penalize at.
decay_ratio: how much to decay penalty as images move apart in batch.
Returns:
Objective. | lucid/optvis/objectives.py | def alignment(layer, decay_ratio=2):
"""Encourage neighboring images to be similar.
When visualizing the interpolation between two objectives, it's often
desireable to encourage analagous boejcts to be drawn in the same position,
to make them more comparable.
This term penalizes L2 distance between neighboring images, as evaluated at
layer.
In general, we find this most effective if used with a paramaterization that
shares across the batch. (In fact, that works quite well by iteself, so this
function may just be obselete.)
Args:
layer: layer to penalize at.
decay_ratio: how much to decay penalty as images move apart in batch.
Returns:
Objective.
"""
def inner(T):
batch_n = T(layer).get_shape().as_list()[0]
arr = T(layer)
accum = 0
for d in [1, 2, 3, 4]:
for i in range(batch_n - d):
a, b = i, i+d
arr1, arr2 = arr[a], arr[b]
accum += tf.reduce_mean((arr1-arr2)**2) / decay_ratio**float(d)
return -accum
return inner | def alignment(layer, decay_ratio=2):
"""Encourage neighboring images to be similar.
When visualizing the interpolation between two objectives, it's often
desireable to encourage analagous boejcts to be drawn in the same position,
to make them more comparable.
This term penalizes L2 distance between neighboring images, as evaluated at
layer.
In general, we find this most effective if used with a paramaterization that
shares across the batch. (In fact, that works quite well by iteself, so this
function may just be obselete.)
Args:
layer: layer to penalize at.
decay_ratio: how much to decay penalty as images move apart in batch.
Returns:
Objective.
"""
def inner(T):
batch_n = T(layer).get_shape().as_list()[0]
arr = T(layer)
accum = 0
for d in [1, 2, 3, 4]:
for i in range(batch_n - d):
a, b = i, i+d
arr1, arr2 = arr[a], arr[b]
accum += tf.reduce_mean((arr1-arr2)**2) / decay_ratio**float(d)
return -accum
return inner | [
"Encourage",
"neighboring",
"images",
"to",
"be",
"similar",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L362-L393 | [
"def",
"alignment",
"(",
"layer",
",",
"decay_ratio",
"=",
"2",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"batch_n",
"=",
"T",
"(",
"layer",
")",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"[",
"0",
"]",
"arr",
"=",
"T",
"(",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | diversity | Encourage diversity between each batch element.
A neural net feature often responds to multiple things, but naive feature
visualization often only shows us one. If you optimize a batch of images,
this objective will encourage them all to be different.
In particular, it caculuates the correlation matrix of activations at layer
for each image, and then penalizes cossine similarity between them. This is
very similar to ideas in style transfer, except we're *penalizing* style
similarity instead of encouraging it.
Args:
layer: layer to evaluate activation correlations on.
Returns:
Objective. | lucid/optvis/objectives.py | def diversity(layer):
"""Encourage diversity between each batch element.
A neural net feature often responds to multiple things, but naive feature
visualization often only shows us one. If you optimize a batch of images,
this objective will encourage them all to be different.
In particular, it caculuates the correlation matrix of activations at layer
for each image, and then penalizes cossine similarity between them. This is
very similar to ideas in style transfer, except we're *penalizing* style
similarity instead of encouraging it.
Args:
layer: layer to evaluate activation correlations on.
Returns:
Objective.
"""
def inner(T):
layer_t = T(layer)
batch_n, _, _, channels = layer_t.get_shape().as_list()
flattened = tf.reshape(layer_t, [batch_n, -1, channels])
grams = tf.matmul(flattened, flattened, transpose_a=True)
grams = tf.nn.l2_normalize(grams, axis=[1,2], epsilon=1e-10)
return sum([ sum([ tf.reduce_sum(grams[i]*grams[j])
for j in range(batch_n) if j != i])
for i in range(batch_n)]) / batch_n
return inner | def diversity(layer):
"""Encourage diversity between each batch element.
A neural net feature often responds to multiple things, but naive feature
visualization often only shows us one. If you optimize a batch of images,
this objective will encourage them all to be different.
In particular, it caculuates the correlation matrix of activations at layer
for each image, and then penalizes cossine similarity between them. This is
very similar to ideas in style transfer, except we're *penalizing* style
similarity instead of encouraging it.
Args:
layer: layer to evaluate activation correlations on.
Returns:
Objective.
"""
def inner(T):
layer_t = T(layer)
batch_n, _, _, channels = layer_t.get_shape().as_list()
flattened = tf.reshape(layer_t, [batch_n, -1, channels])
grams = tf.matmul(flattened, flattened, transpose_a=True)
grams = tf.nn.l2_normalize(grams, axis=[1,2], epsilon=1e-10)
return sum([ sum([ tf.reduce_sum(grams[i]*grams[j])
for j in range(batch_n) if j != i])
for i in range(batch_n)]) / batch_n
return inner | [
"Encourage",
"diversity",
"between",
"each",
"batch",
"element",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L396-L425 | [
"def",
"diversity",
"(",
"layer",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"layer_t",
"=",
"T",
"(",
"layer",
")",
"batch_n",
",",
"_",
",",
"_",
",",
"channels",
"=",
"layer_t",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"flatten... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | input_diff | Average L2 difference between optimized image and orig_img.
This objective is usually mutliplied by a negative number and used as a
penalty in making advarsarial counterexamples. | lucid/optvis/objectives.py | def input_diff(orig_img):
"""Average L2 difference between optimized image and orig_img.
This objective is usually mutliplied by a negative number and used as a
penalty in making advarsarial counterexamples.
"""
def inner(T):
diff = T("input") - orig_img
return tf.sqrt(tf.reduce_mean(diff**2))
return inner | def input_diff(orig_img):
"""Average L2 difference between optimized image and orig_img.
This objective is usually mutliplied by a negative number and used as a
penalty in making advarsarial counterexamples.
"""
def inner(T):
diff = T("input") - orig_img
return tf.sqrt(tf.reduce_mean(diff**2))
return inner | [
"Average",
"L2",
"difference",
"between",
"optimized",
"image",
"and",
"orig_img",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L429-L438 | [
"def",
"input_diff",
"(",
"orig_img",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"diff",
"=",
"T",
"(",
"\"input\"",
")",
"-",
"orig_img",
"return",
"tf",
".",
"sqrt",
"(",
"tf",
".",
"reduce_mean",
"(",
"diff",
"**",
"2",
")",
")",
"return",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | class_logit | Like channel, but for softmax layers.
Args:
layer: A layer name string.
label: Either a string (refering to a label in model.labels) or an int
label position.
Returns:
Objective maximizing a logit. | lucid/optvis/objectives.py | def class_logit(layer, label):
"""Like channel, but for softmax layers.
Args:
layer: A layer name string.
label: Either a string (refering to a label in model.labels) or an int
label position.
Returns:
Objective maximizing a logit.
"""
def inner(T):
if isinstance(label, int):
class_n = label
else:
class_n = T("labels").index(label)
logits = T(layer)
logit = tf.reduce_sum(logits[:, class_n])
return logit
return inner | def class_logit(layer, label):
"""Like channel, but for softmax layers.
Args:
layer: A layer name string.
label: Either a string (refering to a label in model.labels) or an int
label position.
Returns:
Objective maximizing a logit.
"""
def inner(T):
if isinstance(label, int):
class_n = label
else:
class_n = T("labels").index(label)
logits = T(layer)
logit = tf.reduce_sum(logits[:, class_n])
return logit
return inner | [
"Like",
"channel",
"but",
"for",
"softmax",
"layers",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L442-L461 | [
"def",
"class_logit",
"(",
"layer",
",",
"label",
")",
":",
"def",
"inner",
"(",
"T",
")",
":",
"if",
"isinstance",
"(",
"label",
",",
"int",
")",
":",
"class_n",
"=",
"label",
"else",
":",
"class_n",
"=",
"T",
"(",
"\"labels\"",
")",
".",
"index",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | as_objective | Convert obj into Objective class.
Strings of the form "layer:n" become the Objective channel(layer, n).
Objectives are returned unchanged.
Args:
obj: string or Objective.
Returns:
Objective | lucid/optvis/objectives.py | def as_objective(obj):
"""Convert obj into Objective class.
Strings of the form "layer:n" become the Objective channel(layer, n).
Objectives are returned unchanged.
Args:
obj: string or Objective.
Returns:
Objective
"""
if isinstance(obj, Objective):
return obj
elif callable(obj):
return obj
elif isinstance(obj, str):
layer, n = obj.split(":")
layer, n = layer.strip(), int(n)
return channel(layer, n) | def as_objective(obj):
"""Convert obj into Objective class.
Strings of the form "layer:n" become the Objective channel(layer, n).
Objectives are returned unchanged.
Args:
obj: string or Objective.
Returns:
Objective
"""
if isinstance(obj, Objective):
return obj
elif callable(obj):
return obj
elif isinstance(obj, str):
layer, n = obj.split(":")
layer, n = layer.strip(), int(n)
return channel(layer, n) | [
"Convert",
"obj",
"into",
"Objective",
"class",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/objectives.py#L464-L483 | [
"def",
"as_objective",
"(",
"obj",
")",
":",
"if",
"isinstance",
"(",
"obj",
",",
"Objective",
")",
":",
"return",
"obj",
"elif",
"callable",
"(",
"obj",
")",
":",
"return",
"obj",
"elif",
"isinstance",
"(",
"obj",
",",
"str",
")",
":",
"layer",
",",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _constrain_L2_grad | Gradient for constrained optimization on an L2 unit ball.
This function projects the gradient onto the ball if you are on the boundary
(or outside!), but leaves it untouched if you are inside the ball.
Args:
op: the tensorflow op we're computing the gradient for.
grad: gradient we need to backprop
Returns:
(projected if necessary) gradient. | lucid/optvis/param/unit_balls.py | def _constrain_L2_grad(op, grad):
"""Gradient for constrained optimization on an L2 unit ball.
This function projects the gradient onto the ball if you are on the boundary
(or outside!), but leaves it untouched if you are inside the ball.
Args:
op: the tensorflow op we're computing the gradient for.
grad: gradient we need to backprop
Returns:
(projected if necessary) gradient.
"""
inp = op.inputs[0]
inp_norm = tf.norm(inp)
unit_inp = inp / inp_norm
grad_projection = dot(unit_inp, grad)
parallel_grad = unit_inp * grad_projection
is_in_ball = tf.less_equal(inp_norm, 1)
is_pointed_inward = tf.less(grad_projection, 0)
allow_grad = tf.logical_or(is_in_ball, is_pointed_inward)
clip_grad = tf.logical_not(allow_grad)
clipped_grad = tf.cond(clip_grad, lambda: grad - parallel_grad, lambda: grad)
return clipped_grad | def _constrain_L2_grad(op, grad):
"""Gradient for constrained optimization on an L2 unit ball.
This function projects the gradient onto the ball if you are on the boundary
(or outside!), but leaves it untouched if you are inside the ball.
Args:
op: the tensorflow op we're computing the gradient for.
grad: gradient we need to backprop
Returns:
(projected if necessary) gradient.
"""
inp = op.inputs[0]
inp_norm = tf.norm(inp)
unit_inp = inp / inp_norm
grad_projection = dot(unit_inp, grad)
parallel_grad = unit_inp * grad_projection
is_in_ball = tf.less_equal(inp_norm, 1)
is_pointed_inward = tf.less(grad_projection, 0)
allow_grad = tf.logical_or(is_in_ball, is_pointed_inward)
clip_grad = tf.logical_not(allow_grad)
clipped_grad = tf.cond(clip_grad, lambda: grad - parallel_grad, lambda: grad)
return clipped_grad | [
"Gradient",
"for",
"constrained",
"optimization",
"on",
"an",
"L2",
"unit",
"ball",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/unit_balls.py#L20-L47 | [
"def",
"_constrain_L2_grad",
"(",
"op",
",",
"grad",
")",
":",
"inp",
"=",
"op",
".",
"inputs",
"[",
"0",
"]",
"inp_norm",
"=",
"tf",
".",
"norm",
"(",
"inp",
")",
"unit_inp",
"=",
"inp",
"/",
"inp_norm",
"grad_projection",
"=",
"dot",
"(",
"unit_inp... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | unit_ball_L2 | A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code. | lucid/optvis/param/unit_balls.py | def unit_ball_L2(shape):
"""A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
return constrain_L2(x) | def unit_ball_L2(shape):
"""A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
return constrain_L2(x) | [
"A",
"tensorflow",
"variable",
"tranfomed",
"to",
"be",
"constrained",
"in",
"a",
"L2",
"unit",
"ball",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/unit_balls.py#L55-L62 | [
"def",
"unit_ball_L2",
"(",
"shape",
")",
":",
"x",
"=",
"tf",
".",
"Variable",
"(",
"tf",
".",
"zeros",
"(",
"shape",
")",
")",
"return",
"constrain_L2",
"(",
"x",
")"
] | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | unit_ball_L_inf | A tensorflow variable tranfomed to be constrained in a L_inf unit ball.
Note that this code also preconditions the gradient to go in the L_inf
direction of steepest descent.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code. | lucid/optvis/param/unit_balls.py | def unit_ball_L_inf(shape, precondition=True):
"""A tensorflow variable tranfomed to be constrained in a L_inf unit ball.
Note that this code also preconditions the gradient to go in the L_inf
direction of steepest descent.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
if precondition:
return constrain_L_inf_precondition(x)
else:
return constrain_L_inf(x) | def unit_ball_L_inf(shape, precondition=True):
"""A tensorflow variable tranfomed to be constrained in a L_inf unit ball.
Note that this code also preconditions the gradient to go in the L_inf
direction of steepest descent.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
if precondition:
return constrain_L_inf_precondition(x)
else:
return constrain_L_inf(x) | [
"A",
"tensorflow",
"variable",
"tranfomed",
"to",
"be",
"constrained",
"in",
"a",
"L_inf",
"unit",
"ball",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/param/unit_balls.py#L106-L119 | [
"def",
"unit_ball_L_inf",
"(",
"shape",
",",
"precondition",
"=",
"True",
")",
":",
"x",
"=",
"tf",
".",
"Variable",
"(",
"tf",
".",
"zeros",
"(",
"shape",
")",
")",
"if",
"precondition",
":",
"return",
"constrain_L_inf_precondition",
"(",
"x",
")",
"els... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | render_vis | Flexible optimization-base feature vis.
There's a lot of ways one might wish to customize otpimization-based
feature visualization. It's hard to create an abstraction that stands up
to all the things one might wish to try.
This function probably can't do *everything* you want, but it's much more
flexible than a naive attempt. The basic abstraction is to split the problem
into several parts. Consider the rguments:
Args:
model: The model to be visualized, from Alex' modelzoo.
objective_f: The objective our visualization maximizes.
See the objectives module for more details.
param_f: Paramaterization of the image we're optimizing.
See the paramaterization module for more details.
Defaults to a naively paramaterized [1, 128, 128, 3] image.
optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
or a function from (graph, sess) to such an instance.
Defaults to Adam with lr .05.
transforms: A list of stochastic transformations that get composed,
which our visualization should robustly activate the network against.
See the transform module for more details.
Defaults to [transform.jitter(8)].
thresholds: A list of numbers of optimization steps, at which we should
save (and display if verbose=True) the visualization.
print_objectives: A list of objectives separate from those being optimized,
whose values get logged during the optimization.
verbose: Should we display the visualization when we hit a threshold?
This should only be used in IPython.
relu_gradient_override: Whether to use the gradient override scheme
described in lucid/misc/redirected_relu_grad.py. On by default!
use_fixed_seed: Seed the RNG with a fixed value so results are reproducible.
Off by default. As of tf 1.8 this does not work as intended, see:
https://github.com/tensorflow/tensorflow/issues/9171
Returns:
2D array of optimization results containing of evaluations of supplied
param_f snapshotted at specified thresholds. Usually that will mean one or
multiple channel visualizations stacked on top of each other. | lucid/optvis/render.py | def render_vis(model, objective_f, param_f=None, optimizer=None,
transforms=None, thresholds=(512,), print_objectives=None,
verbose=True, relu_gradient_override=True, use_fixed_seed=False):
"""Flexible optimization-base feature vis.
There's a lot of ways one might wish to customize otpimization-based
feature visualization. It's hard to create an abstraction that stands up
to all the things one might wish to try.
This function probably can't do *everything* you want, but it's much more
flexible than a naive attempt. The basic abstraction is to split the problem
into several parts. Consider the rguments:
Args:
model: The model to be visualized, from Alex' modelzoo.
objective_f: The objective our visualization maximizes.
See the objectives module for more details.
param_f: Paramaterization of the image we're optimizing.
See the paramaterization module for more details.
Defaults to a naively paramaterized [1, 128, 128, 3] image.
optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
or a function from (graph, sess) to such an instance.
Defaults to Adam with lr .05.
transforms: A list of stochastic transformations that get composed,
which our visualization should robustly activate the network against.
See the transform module for more details.
Defaults to [transform.jitter(8)].
thresholds: A list of numbers of optimization steps, at which we should
save (and display if verbose=True) the visualization.
print_objectives: A list of objectives separate from those being optimized,
whose values get logged during the optimization.
verbose: Should we display the visualization when we hit a threshold?
This should only be used in IPython.
relu_gradient_override: Whether to use the gradient override scheme
described in lucid/misc/redirected_relu_grad.py. On by default!
use_fixed_seed: Seed the RNG with a fixed value so results are reproducible.
Off by default. As of tf 1.8 this does not work as intended, see:
https://github.com/tensorflow/tensorflow/issues/9171
Returns:
2D array of optimization results containing of evaluations of supplied
param_f snapshotted at specified thresholds. Usually that will mean one or
multiple channel visualizations stacked on top of each other.
"""
with tf.Graph().as_default() as graph, tf.Session() as sess:
if use_fixed_seed: # does not mean results are reproducible, see Args doc
tf.set_random_seed(0)
T = make_vis_T(model, objective_f, param_f, optimizer, transforms,
relu_gradient_override)
print_objective_func = make_print_objective_func(print_objectives, T)
loss, vis_op, t_image = T("loss"), T("vis_op"), T("input")
tf.global_variables_initializer().run()
images = []
try:
for i in range(max(thresholds)+1):
loss_, _ = sess.run([loss, vis_op])
if i in thresholds:
vis = t_image.eval()
images.append(vis)
if verbose:
print(i, loss_)
print_objective_func(sess)
show(np.hstack(vis))
except KeyboardInterrupt:
log.warning("Interrupted optimization at step {:d}.".format(i+1))
vis = t_image.eval()
show(np.hstack(vis))
return images | def render_vis(model, objective_f, param_f=None, optimizer=None,
transforms=None, thresholds=(512,), print_objectives=None,
verbose=True, relu_gradient_override=True, use_fixed_seed=False):
"""Flexible optimization-base feature vis.
There's a lot of ways one might wish to customize otpimization-based
feature visualization. It's hard to create an abstraction that stands up
to all the things one might wish to try.
This function probably can't do *everything* you want, but it's much more
flexible than a naive attempt. The basic abstraction is to split the problem
into several parts. Consider the rguments:
Args:
model: The model to be visualized, from Alex' modelzoo.
objective_f: The objective our visualization maximizes.
See the objectives module for more details.
param_f: Paramaterization of the image we're optimizing.
See the paramaterization module for more details.
Defaults to a naively paramaterized [1, 128, 128, 3] image.
optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
or a function from (graph, sess) to such an instance.
Defaults to Adam with lr .05.
transforms: A list of stochastic transformations that get composed,
which our visualization should robustly activate the network against.
See the transform module for more details.
Defaults to [transform.jitter(8)].
thresholds: A list of numbers of optimization steps, at which we should
save (and display if verbose=True) the visualization.
print_objectives: A list of objectives separate from those being optimized,
whose values get logged during the optimization.
verbose: Should we display the visualization when we hit a threshold?
This should only be used in IPython.
relu_gradient_override: Whether to use the gradient override scheme
described in lucid/misc/redirected_relu_grad.py. On by default!
use_fixed_seed: Seed the RNG with a fixed value so results are reproducible.
Off by default. As of tf 1.8 this does not work as intended, see:
https://github.com/tensorflow/tensorflow/issues/9171
Returns:
2D array of optimization results containing of evaluations of supplied
param_f snapshotted at specified thresholds. Usually that will mean one or
multiple channel visualizations stacked on top of each other.
"""
with tf.Graph().as_default() as graph, tf.Session() as sess:
if use_fixed_seed: # does not mean results are reproducible, see Args doc
tf.set_random_seed(0)
T = make_vis_T(model, objective_f, param_f, optimizer, transforms,
relu_gradient_override)
print_objective_func = make_print_objective_func(print_objectives, T)
loss, vis_op, t_image = T("loss"), T("vis_op"), T("input")
tf.global_variables_initializer().run()
images = []
try:
for i in range(max(thresholds)+1):
loss_, _ = sess.run([loss, vis_op])
if i in thresholds:
vis = t_image.eval()
images.append(vis)
if verbose:
print(i, loss_)
print_objective_func(sess)
show(np.hstack(vis))
except KeyboardInterrupt:
log.warning("Interrupted optimization at step {:d}.".format(i+1))
vis = t_image.eval()
show(np.hstack(vis))
return images | [
"Flexible",
"optimization",
"-",
"base",
"feature",
"vis",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/render.py#L44-L115 | [
"def",
"render_vis",
"(",
"model",
",",
"objective_f",
",",
"param_f",
"=",
"None",
",",
"optimizer",
"=",
"None",
",",
"transforms",
"=",
"None",
",",
"thresholds",
"=",
"(",
"512",
",",
")",
",",
"print_objectives",
"=",
"None",
",",
"verbose",
"=",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | make_vis_T | Even more flexible optimization-base feature vis.
This function is the inner core of render_vis(), and can be used
when render_vis() isn't flexible enough. Unfortunately, it's a bit more
tedious to use:
> with tf.Graph().as_default() as graph, tf.Session() as sess:
>
> T = make_vis_T(model, "mixed4a_pre_relu:0")
> tf.initialize_all_variables().run()
>
> for i in range(10):
> T("vis_op").run()
> showarray(T("input").eval()[0])
This approach allows more control over how the visualizaiton is displayed
as it renders. It also allows a lot more flexibility in constructing
objectives / params because the session is already in scope.
Args:
model: The model to be visualized, from Alex' modelzoo.
objective_f: The objective our visualization maximizes.
See the objectives module for more details.
param_f: Paramaterization of the image we're optimizing.
See the paramaterization module for more details.
Defaults to a naively paramaterized [1, 128, 128, 3] image.
optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
or a function from (graph, sess) to such an instance.
Defaults to Adam with lr .05.
transforms: A list of stochastic transformations that get composed,
which our visualization should robustly activate the network against.
See the transform module for more details.
Defaults to [transform.jitter(8)].
Returns:
A function T, which allows access to:
* T("vis_op") -- the operation for to optimize the visualization
* T("input") -- the visualization itself
* T("loss") -- the loss for the visualization
* T(layer) -- any layer inside the network | lucid/optvis/render.py | def make_vis_T(model, objective_f, param_f=None, optimizer=None,
transforms=None, relu_gradient_override=False):
"""Even more flexible optimization-base feature vis.
This function is the inner core of render_vis(), and can be used
when render_vis() isn't flexible enough. Unfortunately, it's a bit more
tedious to use:
> with tf.Graph().as_default() as graph, tf.Session() as sess:
>
> T = make_vis_T(model, "mixed4a_pre_relu:0")
> tf.initialize_all_variables().run()
>
> for i in range(10):
> T("vis_op").run()
> showarray(T("input").eval()[0])
This approach allows more control over how the visualizaiton is displayed
as it renders. It also allows a lot more flexibility in constructing
objectives / params because the session is already in scope.
Args:
model: The model to be visualized, from Alex' modelzoo.
objective_f: The objective our visualization maximizes.
See the objectives module for more details.
param_f: Paramaterization of the image we're optimizing.
See the paramaterization module for more details.
Defaults to a naively paramaterized [1, 128, 128, 3] image.
optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
or a function from (graph, sess) to such an instance.
Defaults to Adam with lr .05.
transforms: A list of stochastic transformations that get composed,
which our visualization should robustly activate the network against.
See the transform module for more details.
Defaults to [transform.jitter(8)].
Returns:
A function T, which allows access to:
* T("vis_op") -- the operation for to optimize the visualization
* T("input") -- the visualization itself
* T("loss") -- the loss for the visualization
* T(layer) -- any layer inside the network
"""
# pylint: disable=unused-variable
t_image = make_t_image(param_f)
objective_f = objectives.as_objective(objective_f)
transform_f = make_transform_f(transforms)
optimizer = make_optimizer(optimizer, [])
global_step = tf.train.get_or_create_global_step()
init_global_step = tf.variables_initializer([global_step])
init_global_step.run()
if relu_gradient_override:
with gradient_override_map({'Relu': redirected_relu_grad,
'Relu6': redirected_relu6_grad}):
T = import_model(model, transform_f(t_image), t_image)
else:
T = import_model(model, transform_f(t_image), t_image)
loss = objective_f(T)
vis_op = optimizer.minimize(-loss, global_step=global_step)
local_vars = locals()
# pylint: enable=unused-variable
def T2(name):
if name in local_vars:
return local_vars[name]
else: return T(name)
return T2 | def make_vis_T(model, objective_f, param_f=None, optimizer=None,
transforms=None, relu_gradient_override=False):
"""Even more flexible optimization-base feature vis.
This function is the inner core of render_vis(), and can be used
when render_vis() isn't flexible enough. Unfortunately, it's a bit more
tedious to use:
> with tf.Graph().as_default() as graph, tf.Session() as sess:
>
> T = make_vis_T(model, "mixed4a_pre_relu:0")
> tf.initialize_all_variables().run()
>
> for i in range(10):
> T("vis_op").run()
> showarray(T("input").eval()[0])
This approach allows more control over how the visualizaiton is displayed
as it renders. It also allows a lot more flexibility in constructing
objectives / params because the session is already in scope.
Args:
model: The model to be visualized, from Alex' modelzoo.
objective_f: The objective our visualization maximizes.
See the objectives module for more details.
param_f: Paramaterization of the image we're optimizing.
See the paramaterization module for more details.
Defaults to a naively paramaterized [1, 128, 128, 3] image.
optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
or a function from (graph, sess) to such an instance.
Defaults to Adam with lr .05.
transforms: A list of stochastic transformations that get composed,
which our visualization should robustly activate the network against.
See the transform module for more details.
Defaults to [transform.jitter(8)].
Returns:
A function T, which allows access to:
* T("vis_op") -- the operation for to optimize the visualization
* T("input") -- the visualization itself
* T("loss") -- the loss for the visualization
* T(layer) -- any layer inside the network
"""
# pylint: disable=unused-variable
t_image = make_t_image(param_f)
objective_f = objectives.as_objective(objective_f)
transform_f = make_transform_f(transforms)
optimizer = make_optimizer(optimizer, [])
global_step = tf.train.get_or_create_global_step()
init_global_step = tf.variables_initializer([global_step])
init_global_step.run()
if relu_gradient_override:
with gradient_override_map({'Relu': redirected_relu_grad,
'Relu6': redirected_relu6_grad}):
T = import_model(model, transform_f(t_image), t_image)
else:
T = import_model(model, transform_f(t_image), t_image)
loss = objective_f(T)
vis_op = optimizer.minimize(-loss, global_step=global_step)
local_vars = locals()
# pylint: enable=unused-variable
def T2(name):
if name in local_vars:
return local_vars[name]
else: return T(name)
return T2 | [
"Even",
"more",
"flexible",
"optimization",
"-",
"base",
"feature",
"vis",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/render.py#L118-L192 | [
"def",
"make_vis_T",
"(",
"model",
",",
"objective_f",
",",
"param_f",
"=",
"None",
",",
"optimizer",
"=",
"None",
",",
"transforms",
"=",
"None",
",",
"relu_gradient_override",
"=",
"False",
")",
":",
"# pylint: disable=unused-variable",
"t_image",
"=",
"make_t... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | grid | layout: numpy arrays x, y
metadata: user-defined numpy arrays with metadata
n_layer: number of cells in the layer (squared)
n_tile: number of cells in the tile (squared) | lucid/scratch/atlas_pipeline/grid.py | def grid(metadata, layout, params):
"""
layout: numpy arrays x, y
metadata: user-defined numpy arrays with metadata
n_layer: number of cells in the layer (squared)
n_tile: number of cells in the tile (squared)
"""
x = layout["x"]
y = layout["y"]
x_min = np.min(x)
x_max = np.max(x)
y_min = np.min(y)
y_max = np.max(y)
# this creates the grid
bins = np.linspace(x_min, x_max, params["n_layer"] - 1)
xd = np.digitize(x, bins)
bins = np.linspace(y_min, y_max, params["n_layer"] - 1)
yd = np.digitize(y, bins)
# the number of tiles is the number of cells divided by the number of cells in each tile
num_tiles = int(params["n_layer"]/params["n_tile"])
print("num tiles", num_tiles)
# we will save the tiles in an array indexed by the tile coordinates
tiles = {}
for ti in range(num_tiles):
for tj in range(num_tiles):
tiles[(ti,tj)] = {
"x": [],
"y": [],
"ci": [], # cell-space x coordinate
"cj": [], # cell-space y coordinate
"gi": [], # global index
}
for i,xi in enumerate(x):
if(i % 1000 == 0 or i+1 == len(x)):
print("point", i+1, "/", len(x), end="\r")
# layout-space coordinates
yi = y[i]
# grid-space cell coordinates
ci = xd[i]
cj = yd[i]
# tile coordinate
ti = math.floor(ci / params["n_tile"])
tj = math.floor(cj / params["n_tile"])
# TODO: don't append a point if it doesn't match a filter function provided in params
filter = params.get("filter", lambda i,metadata: True)
if(filter(i, metadata=metadata)):
tiles[(ti,tj)]["x"].append(xi)
tiles[(ti,tj)]["y"].append(yi)
tiles[(ti,tj)]["ci"].append(ci)
tiles[(ti,tj)]["cj"].append(cj)
tiles[(ti,tj)]["gi"].append(i)
return tiles | def grid(metadata, layout, params):
"""
layout: numpy arrays x, y
metadata: user-defined numpy arrays with metadata
n_layer: number of cells in the layer (squared)
n_tile: number of cells in the tile (squared)
"""
x = layout["x"]
y = layout["y"]
x_min = np.min(x)
x_max = np.max(x)
y_min = np.min(y)
y_max = np.max(y)
# this creates the grid
bins = np.linspace(x_min, x_max, params["n_layer"] - 1)
xd = np.digitize(x, bins)
bins = np.linspace(y_min, y_max, params["n_layer"] - 1)
yd = np.digitize(y, bins)
# the number of tiles is the number of cells divided by the number of cells in each tile
num_tiles = int(params["n_layer"]/params["n_tile"])
print("num tiles", num_tiles)
# we will save the tiles in an array indexed by the tile coordinates
tiles = {}
for ti in range(num_tiles):
for tj in range(num_tiles):
tiles[(ti,tj)] = {
"x": [],
"y": [],
"ci": [], # cell-space x coordinate
"cj": [], # cell-space y coordinate
"gi": [], # global index
}
for i,xi in enumerate(x):
if(i % 1000 == 0 or i+1 == len(x)):
print("point", i+1, "/", len(x), end="\r")
# layout-space coordinates
yi = y[i]
# grid-space cell coordinates
ci = xd[i]
cj = yd[i]
# tile coordinate
ti = math.floor(ci / params["n_tile"])
tj = math.floor(cj / params["n_tile"])
# TODO: don't append a point if it doesn't match a filter function provided in params
filter = params.get("filter", lambda i,metadata: True)
if(filter(i, metadata=metadata)):
tiles[(ti,tj)]["x"].append(xi)
tiles[(ti,tj)]["y"].append(yi)
tiles[(ti,tj)]["ci"].append(ci)
tiles[(ti,tj)]["cj"].append(cj)
tiles[(ti,tj)]["gi"].append(i)
return tiles | [
"layout",
":",
"numpy",
"arrays",
"x",
"y",
"metadata",
":",
"user",
"-",
"defined",
"numpy",
"arrays",
"with",
"metadata",
"n_layer",
":",
"number",
"of",
"cells",
"in",
"the",
"layer",
"(",
"squared",
")",
"n_tile",
":",
"number",
"of",
"cells",
"in",
... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/scratch/atlas_pipeline/grid.py#L12-L68 | [
"def",
"grid",
"(",
"metadata",
",",
"layout",
",",
"params",
")",
":",
"x",
"=",
"layout",
"[",
"\"x\"",
"]",
"y",
"=",
"layout",
"[",
"\"y\"",
"]",
"x_min",
"=",
"np",
".",
"min",
"(",
"x",
")",
"x_max",
"=",
"np",
".",
"max",
"(",
"x",
")"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | write_grid_local | Write a file for each tile | lucid/scratch/atlas_pipeline/grid.py | def write_grid_local(tiles, params):
"""
Write a file for each tile
"""
# TODO: this isn't being used right now, will need to be
# ported to gfile if we want to keep it
for ti,tj,tile in enumerate_tiles(tiles):
filename = "{directory}/{name}/tile_{n_layer}_{n_tile}_{ti}_{tj}".format(ti=ti, tj=tj, **params) #directory=directory, name=name, n_layer=n_layer, n_tile=n_tile,
# write out the tile as a npz
print("saving", filename + ".npz")
np.savez_compressed(filename + ".npz", **tile)
# write out the tile as a csv
print("saving", filename + ".csv")
df = pd.DataFrame(tile)
df.to_csv(filename + ".csv", index=False) | def write_grid_local(tiles, params):
"""
Write a file for each tile
"""
# TODO: this isn't being used right now, will need to be
# ported to gfile if we want to keep it
for ti,tj,tile in enumerate_tiles(tiles):
filename = "{directory}/{name}/tile_{n_layer}_{n_tile}_{ti}_{tj}".format(ti=ti, tj=tj, **params) #directory=directory, name=name, n_layer=n_layer, n_tile=n_tile,
# write out the tile as a npz
print("saving", filename + ".npz")
np.savez_compressed(filename + ".npz", **tile)
# write out the tile as a csv
print("saving", filename + ".csv")
df = pd.DataFrame(tile)
df.to_csv(filename + ".csv", index=False) | [
"Write",
"a",
"file",
"for",
"each",
"tile"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/scratch/atlas_pipeline/grid.py#L70-L84 | [
"def",
"write_grid_local",
"(",
"tiles",
",",
"params",
")",
":",
"# TODO: this isn't being used right now, will need to be",
"# ported to gfile if we want to keep it",
"for",
"ti",
",",
"tj",
",",
"tile",
"in",
"enumerate_tiles",
"(",
"tiles",
")",
":",
"filename",
"="... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | enumerate_tiles | Convenience | lucid/scratch/atlas_pipeline/grid.py | def enumerate_tiles(tiles):
"""
Convenience
"""
enumerated = []
for key in tiles.keys():
enumerated.append((key[0], key[1], tiles[key]))
return enumerated | def enumerate_tiles(tiles):
"""
Convenience
"""
enumerated = []
for key in tiles.keys():
enumerated.append((key[0], key[1], tiles[key]))
return enumerated | [
"Convenience"
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/scratch/atlas_pipeline/grid.py#L86-L93 | [
"def",
"enumerate_tiles",
"(",
"tiles",
")",
":",
"enumerated",
"=",
"[",
"]",
"for",
"key",
"in",
"tiles",
".",
"keys",
"(",
")",
":",
"enumerated",
".",
"append",
"(",
"(",
"key",
"[",
"0",
"]",
",",
"key",
"[",
"1",
"]",
",",
"tiles",
"[",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _load_img | Load image file as numpy array. | lucid/misc/io/loading.py | def _load_img(handle, target_dtype=np.float32, size=None, **kwargs):
"""Load image file as numpy array."""
image_pil = PIL.Image.open(handle, **kwargs)
# resize the image to the requested size, if one was specified
if size is not None:
if len(size) > 2:
size = size[:2]
log.warning("`_load_img()` received size: {}, trimming to first two dims!".format(size))
image_pil = image_pil.resize(size, resample=PIL.Image.LANCZOS)
image_array = np.asarray(image_pil)
# remove alpha channel if it contains no information
# if image_array.shape[-1] > 3 and 'A' not in image_pil.mode:
# image_array = image_array[..., :-1]
image_dtype = image_array.dtype
image_max_value = np.iinfo(image_dtype).max # ...for uint8 that's 255, etc.
# using np.divide should avoid an extra copy compared to doing division first
ndimage = np.divide(image_array, image_max_value, dtype=target_dtype)
rank = len(ndimage.shape)
if rank == 3:
return ndimage
elif rank == 2:
return np.repeat(np.expand_dims(ndimage, axis=2), 3, axis=2)
else:
message = "Loaded image has more dimensions than expected: {}".format(rank)
raise NotImplementedError(message) | def _load_img(handle, target_dtype=np.float32, size=None, **kwargs):
"""Load image file as numpy array."""
image_pil = PIL.Image.open(handle, **kwargs)
# resize the image to the requested size, if one was specified
if size is not None:
if len(size) > 2:
size = size[:2]
log.warning("`_load_img()` received size: {}, trimming to first two dims!".format(size))
image_pil = image_pil.resize(size, resample=PIL.Image.LANCZOS)
image_array = np.asarray(image_pil)
# remove alpha channel if it contains no information
# if image_array.shape[-1] > 3 and 'A' not in image_pil.mode:
# image_array = image_array[..., :-1]
image_dtype = image_array.dtype
image_max_value = np.iinfo(image_dtype).max # ...for uint8 that's 255, etc.
# using np.divide should avoid an extra copy compared to doing division first
ndimage = np.divide(image_array, image_max_value, dtype=target_dtype)
rank = len(ndimage.shape)
if rank == 3:
return ndimage
elif rank == 2:
return np.repeat(np.expand_dims(ndimage, axis=2), 3, axis=2)
else:
message = "Loaded image has more dimensions than expected: {}".format(rank)
raise NotImplementedError(message) | [
"Load",
"image",
"file",
"as",
"numpy",
"array",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/loading.py#L47-L78 | [
"def",
"_load_img",
"(",
"handle",
",",
"target_dtype",
"=",
"np",
".",
"float32",
",",
"size",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"image_pil",
"=",
"PIL",
".",
"Image",
".",
"open",
"(",
"handle",
",",
"*",
"*",
"kwargs",
")",
"# resi... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _load_text | Load and decode a string. | lucid/misc/io/loading.py | def _load_text(handle, split=False, encoding="utf-8"):
"""Load and decode a string."""
string = handle.read().decode(encoding)
return string.splitlines() if split else string | def _load_text(handle, split=False, encoding="utf-8"):
"""Load and decode a string."""
string = handle.read().decode(encoding)
return string.splitlines() if split else string | [
"Load",
"and",
"decode",
"a",
"string",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/loading.py#L86-L89 | [
"def",
"_load_text",
"(",
"handle",
",",
"split",
"=",
"False",
",",
"encoding",
"=",
"\"utf-8\"",
")",
":",
"string",
"=",
"handle",
".",
"read",
"(",
")",
".",
"decode",
"(",
"encoding",
")",
"return",
"string",
".",
"splitlines",
"(",
")",
"if",
"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _load_graphdef_protobuf | Load GraphDef from a binary proto file. | lucid/misc/io/loading.py | def _load_graphdef_protobuf(handle, **kwargs):
"""Load GraphDef from a binary proto file."""
# as_graph_def
graph_def = tf.GraphDef.FromString(handle.read())
# check if this is a lucid-saved model
# metadata = modelzoo.util.extract_metadata(graph_def)
# if metadata is not None:
# url = handle.name
# return modelzoo.vision_base.Model.load_from_metadata(url, metadata)
# else return a normal graph_def
return graph_def | def _load_graphdef_protobuf(handle, **kwargs):
"""Load GraphDef from a binary proto file."""
# as_graph_def
graph_def = tf.GraphDef.FromString(handle.read())
# check if this is a lucid-saved model
# metadata = modelzoo.util.extract_metadata(graph_def)
# if metadata is not None:
# url = handle.name
# return modelzoo.vision_base.Model.load_from_metadata(url, metadata)
# else return a normal graph_def
return graph_def | [
"Load",
"GraphDef",
"from",
"a",
"binary",
"proto",
"file",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/loading.py#L92-L104 | [
"def",
"_load_graphdef_protobuf",
"(",
"handle",
",",
"*",
"*",
"kwargs",
")",
":",
"# as_graph_def",
"graph_def",
"=",
"tf",
".",
"GraphDef",
".",
"FromString",
"(",
"handle",
".",
"read",
"(",
")",
")",
"# check if this is a lucid-saved model",
"# metadata = mod... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | load | Load a file.
File format is inferred from url. File retrieval strategy is inferred from
URL. Returned object type is inferred from url extension.
Args:
url_or_handle: a (reachable) URL, or an already open file handle
Raises:
RuntimeError: If file extension or URL is not supported. | lucid/misc/io/loading.py | def load(url_or_handle, cache=None, **kwargs):
"""Load a file.
File format is inferred from url. File retrieval strategy is inferred from
URL. Returned object type is inferred from url extension.
Args:
url_or_handle: a (reachable) URL, or an already open file handle
Raises:
RuntimeError: If file extension or URL is not supported.
"""
ext = get_extension(url_or_handle)
try:
loader = loaders[ext.lower()]
message = "Using inferred loader '%s' due to passed file extension '%s'."
log.debug(message, loader.__name__[6:], ext)
return load_using_loader(url_or_handle, loader, cache, **kwargs)
except KeyError:
log.warning("Unknown extension '%s', attempting to load as image.", ext)
try:
with read_handle(url_or_handle, cache=cache) as handle:
result = _load_img(handle)
except Exception as e:
message = "Could not load resource %s as image. Supported extensions: %s"
log.error(message, url_or_handle, list(loaders))
raise RuntimeError(message.format(url_or_handle, list(loaders)))
else:
log.info("Unknown extension '%s' successfully loaded as image.", ext)
return result | def load(url_or_handle, cache=None, **kwargs):
"""Load a file.
File format is inferred from url. File retrieval strategy is inferred from
URL. Returned object type is inferred from url extension.
Args:
url_or_handle: a (reachable) URL, or an already open file handle
Raises:
RuntimeError: If file extension or URL is not supported.
"""
ext = get_extension(url_or_handle)
try:
loader = loaders[ext.lower()]
message = "Using inferred loader '%s' due to passed file extension '%s'."
log.debug(message, loader.__name__[6:], ext)
return load_using_loader(url_or_handle, loader, cache, **kwargs)
except KeyError:
log.warning("Unknown extension '%s', attempting to load as image.", ext)
try:
with read_handle(url_or_handle, cache=cache) as handle:
result = _load_img(handle)
except Exception as e:
message = "Could not load resource %s as image. Supported extensions: %s"
log.error(message, url_or_handle, list(loaders))
raise RuntimeError(message.format(url_or_handle, list(loaders)))
else:
log.info("Unknown extension '%s' successfully loaded as image.", ext)
return result | [
"Load",
"a",
"file",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/loading.py#L120-L152 | [
"def",
"load",
"(",
"url_or_handle",
",",
"cache",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"ext",
"=",
"get_extension",
"(",
"url_or_handle",
")",
"try",
":",
"loader",
"=",
"loaders",
"[",
"ext",
".",
"lower",
"(",
")",
"]",
"message",
"=",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | crop_or_pad_to | Ensures the specified spatial shape by either padding or cropping.
Meant to be used as a last transform for architectures insisting on a specific
spatial shape of their inputs. | lucid/optvis/transform.py | def crop_or_pad_to(height, width):
"""Ensures the specified spatial shape by either padding or cropping.
Meant to be used as a last transform for architectures insisting on a specific
spatial shape of their inputs.
"""
def inner(t_image):
return tf.image.resize_image_with_crop_or_pad(t_image, height, width)
return inner | def crop_or_pad_to(height, width):
"""Ensures the specified spatial shape by either padding or cropping.
Meant to be used as a last transform for architectures insisting on a specific
spatial shape of their inputs.
"""
def inner(t_image):
return tf.image.resize_image_with_crop_or_pad(t_image, height, width)
return inner | [
"Ensures",
"the",
"specified",
"spatial",
"shape",
"by",
"either",
"padding",
"or",
"cropping",
".",
"Meant",
"to",
"be",
"used",
"as",
"a",
"last",
"transform",
"for",
"architectures",
"insisting",
"on",
"a",
"specific",
"spatial",
"shape",
"of",
"their",
"... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/transform.py#L154-L161 | [
"def",
"crop_or_pad_to",
"(",
"height",
",",
"width",
")",
":",
"def",
"inner",
"(",
"t_image",
")",
":",
"return",
"tf",
".",
"image",
".",
"resize_image_with_crop_or_pad",
"(",
"t_image",
",",
"height",
",",
"width",
")",
"return",
"inner"
] | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _normalize_array | Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the array's
own range of values and normalize them.
Returns:
normalized PIL.Image | lucid/misc/io/serialize_array.py | def _normalize_array(array, domain=(0, 1)):
"""Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the array's
own range of values and normalize them.
Returns:
normalized PIL.Image
"""
# first copy the input so we're never mutating the user's data
array = np.array(array)
# squeeze helps both with batch=1 and B/W and PIL's mode inference
array = np.squeeze(array)
assert len(array.shape) <= 3
assert np.issubdtype(array.dtype, np.number)
assert not np.isnan(array).any()
low, high = np.min(array), np.max(array)
if domain is None:
message = "No domain specified, normalizing from measured (~%.2f, ~%.2f)"
log.debug(message, low, high)
domain = (low, high)
# clip values if domain was specified and array contains values outside of it
if low < domain[0] or high > domain[1]:
message = "Clipping domain from (~{:.2f}, ~{:.2f}) to (~{:.2f}, ~{:.2f})."
log.info(message.format(low, high, domain[0], domain[1]))
array = array.clip(*domain)
min_value, max_value = np.iinfo(np.uint8).min, np.iinfo(np.uint8).max # 0, 255
# convert signed to unsigned if needed
if np.issubdtype(array.dtype, np.inexact):
offset = domain[0]
if offset != 0:
array -= offset
log.debug("Converting inexact array by subtracting -%.2f.", offset)
scalar = max_value / (domain[1] - domain[0])
if scalar != 1:
array *= scalar
log.debug("Converting inexact array by scaling by %.2f.", scalar)
return array.clip(min_value, max_value).astype(np.uint8) | def _normalize_array(array, domain=(0, 1)):
"""Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the array's
own range of values and normalize them.
Returns:
normalized PIL.Image
"""
# first copy the input so we're never mutating the user's data
array = np.array(array)
# squeeze helps both with batch=1 and B/W and PIL's mode inference
array = np.squeeze(array)
assert len(array.shape) <= 3
assert np.issubdtype(array.dtype, np.number)
assert not np.isnan(array).any()
low, high = np.min(array), np.max(array)
if domain is None:
message = "No domain specified, normalizing from measured (~%.2f, ~%.2f)"
log.debug(message, low, high)
domain = (low, high)
# clip values if domain was specified and array contains values outside of it
if low < domain[0] or high > domain[1]:
message = "Clipping domain from (~{:.2f}, ~{:.2f}) to (~{:.2f}, ~{:.2f})."
log.info(message.format(low, high, domain[0], domain[1]))
array = array.clip(*domain)
min_value, max_value = np.iinfo(np.uint8).min, np.iinfo(np.uint8).max # 0, 255
# convert signed to unsigned if needed
if np.issubdtype(array.dtype, np.inexact):
offset = domain[0]
if offset != 0:
array -= offset
log.debug("Converting inexact array by subtracting -%.2f.", offset)
scalar = max_value / (domain[1] - domain[0])
if scalar != 1:
array *= scalar
log.debug("Converting inexact array by scaling by %.2f.", scalar)
return array.clip(min_value, max_value).astype(np.uint8) | [
"Given",
"an",
"arbitrary",
"rank",
"-",
"3",
"NumPy",
"array",
"produce",
"one",
"representing",
"an",
"image",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/serialize_array.py#L31-L77 | [
"def",
"_normalize_array",
"(",
"array",
",",
"domain",
"=",
"(",
"0",
",",
"1",
")",
")",
":",
"# first copy the input so we're never mutating the user's data",
"array",
"=",
"np",
".",
"array",
"(",
"array",
")",
"# squeeze helps both with batch=1 and B/W and PIL's mo... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _serialize_normalized_array | Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer | lucid/misc/io/serialize_array.py | def _serialize_normalized_array(array, fmt='png', quality=70):
"""Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
dtype = array.dtype
assert np.issubdtype(dtype, np.unsignedinteger)
assert np.max(array) <= np.iinfo(dtype).max
assert array.shape[-1] > 1 # array dims must have been squeezed
image = PIL.Image.fromarray(array)
image_bytes = BytesIO()
image.save(image_bytes, fmt, quality=quality)
# TODO: Python 3 could save a copy here by using `getbuffer()` instead.
image_data = image_bytes.getvalue()
return image_data | def _serialize_normalized_array(array, fmt='png', quality=70):
"""Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
dtype = array.dtype
assert np.issubdtype(dtype, np.unsignedinteger)
assert np.max(array) <= np.iinfo(dtype).max
assert array.shape[-1] > 1 # array dims must have been squeezed
image = PIL.Image.fromarray(array)
image_bytes = BytesIO()
image.save(image_bytes, fmt, quality=quality)
# TODO: Python 3 could save a copy here by using `getbuffer()` instead.
image_data = image_bytes.getvalue()
return image_data | [
"Given",
"a",
"normalized",
"array",
"returns",
"byte",
"representation",
"of",
"image",
"encoding",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/serialize_array.py#L80-L101 | [
"def",
"_serialize_normalized_array",
"(",
"array",
",",
"fmt",
"=",
"'png'",
",",
"quality",
"=",
"70",
")",
":",
"dtype",
"=",
"array",
".",
"dtype",
"assert",
"np",
".",
"issubdtype",
"(",
"dtype",
",",
"np",
".",
"unsignedinteger",
")",
"assert",
"np... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | serialize_array | Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer | lucid/misc/io/serialize_array.py | def serialize_array(array, domain=(0, 1), fmt='png', quality=70):
"""Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
normalized = _normalize_array(array, domain=domain)
return _serialize_normalized_array(normalized, fmt=fmt, quality=quality) | def serialize_array(array, domain=(0, 1), fmt='png', quality=70):
"""Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer
"""
normalized = _normalize_array(array, domain=domain)
return _serialize_normalized_array(normalized, fmt=fmt, quality=quality) | [
"Given",
"an",
"arbitrary",
"rank",
"-",
"3",
"NumPy",
"array",
"returns",
"the",
"byte",
"representation",
"of",
"the",
"encoded",
"image",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/serialize_array.py#L104-L118 | [
"def",
"serialize_array",
"(",
"array",
",",
"domain",
"=",
"(",
"0",
",",
"1",
")",
",",
"fmt",
"=",
"'png'",
",",
"quality",
"=",
"70",
")",
":",
"normalized",
"=",
"_normalize_array",
"(",
"array",
",",
"domain",
"=",
"domain",
")",
"return",
"_se... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | array_to_jsbuffer | Serialize 1d NumPy array to JS TypedArray.
Data is serialized to base64-encoded string, which is much faster
and memory-efficient than json list serialization.
Args:
array: 1d NumPy array, dtype must be one of JS_ARRAY_TYPES.
Returns:
JS code that evaluates to a TypedArray as string.
Raises:
TypeError: if array dtype or shape not supported. | lucid/misc/io/serialize_array.py | def array_to_jsbuffer(array):
"""Serialize 1d NumPy array to JS TypedArray.
Data is serialized to base64-encoded string, which is much faster
and memory-efficient than json list serialization.
Args:
array: 1d NumPy array, dtype must be one of JS_ARRAY_TYPES.
Returns:
JS code that evaluates to a TypedArray as string.
Raises:
TypeError: if array dtype or shape not supported.
"""
if array.ndim != 1:
raise TypeError('Only 1d arrays can be converted JS TypedArray.')
if array.dtype.name not in JS_ARRAY_TYPES:
raise TypeError('Array dtype not supported by JS TypedArray.')
js_type_name = array.dtype.name.capitalize() + 'Array'
data_base64 = base64.b64encode(array.tobytes()).decode('ascii')
code = """
(function() {
const data = atob("%s");
const buf = new Uint8Array(data.length);
for (var i=0; i<data.length; ++i) {
buf[i] = data.charCodeAt(i);
}
var array_type = %s;
if (array_type == Uint8Array) {
return buf;
}
return new array_type(buf.buffer);
})()
""" % (data_base64, js_type_name)
return code | def array_to_jsbuffer(array):
"""Serialize 1d NumPy array to JS TypedArray.
Data is serialized to base64-encoded string, which is much faster
and memory-efficient than json list serialization.
Args:
array: 1d NumPy array, dtype must be one of JS_ARRAY_TYPES.
Returns:
JS code that evaluates to a TypedArray as string.
Raises:
TypeError: if array dtype or shape not supported.
"""
if array.ndim != 1:
raise TypeError('Only 1d arrays can be converted JS TypedArray.')
if array.dtype.name not in JS_ARRAY_TYPES:
raise TypeError('Array dtype not supported by JS TypedArray.')
js_type_name = array.dtype.name.capitalize() + 'Array'
data_base64 = base64.b64encode(array.tobytes()).decode('ascii')
code = """
(function() {
const data = atob("%s");
const buf = new Uint8Array(data.length);
for (var i=0; i<data.length; ++i) {
buf[i] = data.charCodeAt(i);
}
var array_type = %s;
if (array_type == Uint8Array) {
return buf;
}
return new array_type(buf.buffer);
})()
""" % (data_base64, js_type_name)
return code | [
"Serialize",
"1d",
"NumPy",
"array",
"to",
"JS",
"TypedArray",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/serialize_array.py#L126-L161 | [
"def",
"array_to_jsbuffer",
"(",
"array",
")",
":",
"if",
"array",
".",
"ndim",
"!=",
"1",
":",
"raise",
"TypeError",
"(",
"'Only 1d arrays can be converted JS TypedArray.'",
")",
"if",
"array",
".",
"dtype",
".",
"name",
"not",
"in",
"JS_ARRAY_TYPES",
":",
"r... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | ChannelReducer._apply_flat | Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged. | lucid/misc/channel_reducer.py | def _apply_flat(cls, f, acts):
"""Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged.
"""
orig_shape = acts.shape
acts_flat = acts.reshape([-1, acts.shape[-1]])
new_flat = f(acts_flat)
if not isinstance(new_flat, np.ndarray):
return new_flat
shape = list(orig_shape[:-1]) + [-1]
return new_flat.reshape(shape) | def _apply_flat(cls, f, acts):
"""Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged.
"""
orig_shape = acts.shape
acts_flat = acts.reshape([-1, acts.shape[-1]])
new_flat = f(acts_flat)
if not isinstance(new_flat, np.ndarray):
return new_flat
shape = list(orig_shape[:-1]) + [-1]
return new_flat.reshape(shape) | [
"Utility",
"for",
"applying",
"f",
"to",
"inner",
"dimension",
"of",
"acts",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/channel_reducer.py#L52-L64 | [
"def",
"_apply_flat",
"(",
"cls",
",",
"f",
",",
"acts",
")",
":",
"orig_shape",
"=",
"acts",
".",
"shape",
"acts_flat",
"=",
"acts",
".",
"reshape",
"(",
"[",
"-",
"1",
",",
"acts",
".",
"shape",
"[",
"-",
"1",
"]",
"]",
")",
"new_flat",
"=",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | StyleLoss.set_style | Set target style variables.
Expected usage:
style_loss = StyleLoss(style_layers)
...
init_op = tf.global_variables_initializer()
init_op.run()
feeds = {... session.run() 'feeds' argument that will make 'style_layers'
tensors evaluate to activation values of style image...}
style_loss.set_style(feeds) # this must be called after 'init_op.run()' | lucid/optvis/style.py | def set_style(self, input_feeds):
"""Set target style variables.
Expected usage:
style_loss = StyleLoss(style_layers)
...
init_op = tf.global_variables_initializer()
init_op.run()
feeds = {... session.run() 'feeds' argument that will make 'style_layers'
tensors evaluate to activation values of style image...}
style_loss.set_style(feeds) # this must be called after 'init_op.run()'
"""
sess = tf.get_default_session()
computed = sess.run(self.input_grams, input_feeds)
for v, g in zip(self.target_vars, computed):
v.load(g) | def set_style(self, input_feeds):
"""Set target style variables.
Expected usage:
style_loss = StyleLoss(style_layers)
...
init_op = tf.global_variables_initializer()
init_op.run()
feeds = {... session.run() 'feeds' argument that will make 'style_layers'
tensors evaluate to activation values of style image...}
style_loss.set_style(feeds) # this must be called after 'init_op.run()'
"""
sess = tf.get_default_session()
computed = sess.run(self.input_grams, input_feeds)
for v, g in zip(self.target_vars, computed):
v.load(g) | [
"Set",
"target",
"style",
"variables",
".",
"Expected",
"usage",
":",
"style_loss",
"=",
"StyleLoss",
"(",
"style_layers",
")",
"...",
"init_op",
"=",
"tf",
".",
"global_variables_initializer",
"()",
"init_op",
".",
"run",
"()",
"feeds",
"=",
"{",
"...",
"se... | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/optvis/style.py#L74-L90 | [
"def",
"set_style",
"(",
"self",
",",
"input_feeds",
")",
":",
"sess",
"=",
"tf",
".",
"get_default_session",
"(",
")",
"computed",
"=",
"sess",
".",
"run",
"(",
"self",
".",
"input_grams",
",",
"input_feeds",
")",
"for",
"v",
",",
"g",
"in",
"zip",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | _image_url | Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image | lucid/misc/io/showing.py | def _image_url(array, fmt='png', mode="data", quality=90, domain=None):
"""Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image
"""
supported_modes = ("data")
if mode not in supported_modes:
message = "Unsupported mode '%s', should be one of '%s'."
raise ValueError(message, mode, supported_modes)
image_data = serialize_array(array, fmt=fmt, quality=quality)
base64_byte_string = base64.b64encode(image_data).decode('ascii')
return "data:image/" + fmt.upper() + ";base64," + base64_byte_string | def _image_url(array, fmt='png', mode="data", quality=90, domain=None):
"""Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image
"""
supported_modes = ("data")
if mode not in supported_modes:
message = "Unsupported mode '%s', should be one of '%s'."
raise ValueError(message, mode, supported_modes)
image_data = serialize_array(array, fmt=fmt, quality=quality)
base64_byte_string = base64.b64encode(image_data).decode('ascii')
return "data:image/" + fmt.upper() + ";base64," + base64_byte_string | [
"Create",
"a",
"data",
"URL",
"representing",
"an",
"image",
"from",
"a",
"PIL",
".",
"Image",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/showing.py#L39-L56 | [
"def",
"_image_url",
"(",
"array",
",",
"fmt",
"=",
"'png'",
",",
"mode",
"=",
"\"data\"",
",",
"quality",
"=",
"90",
",",
"domain",
"=",
"None",
")",
":",
"supported_modes",
"=",
"(",
"\"data\"",
")",
"if",
"mode",
"not",
"in",
"supported_modes",
":",... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | image | Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None | lucid/misc/io/showing.py | def image(array, domain=None, width=None, format='png', **kwargs):
"""Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None
"""
image_data = serialize_array(array, fmt=format, domain=domain)
image = IPython.display.Image(data=image_data, format=format, width=width)
IPython.display.display(image) | def image(array, domain=None, width=None, format='png', **kwargs):
"""Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None
"""
image_data = serialize_array(array, fmt=format, domain=domain)
image = IPython.display.Image(data=image_data, format=format, width=width)
IPython.display.display(image) | [
"Display",
"an",
"image",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/showing.py#L62-L75 | [
"def",
"image",
"(",
"array",
",",
"domain",
"=",
"None",
",",
"width",
"=",
"None",
",",
"format",
"=",
"'png'",
",",
"*",
"*",
"kwargs",
")",
":",
"image_data",
"=",
"serialize_array",
"(",
"array",
",",
"fmt",
"=",
"format",
",",
"domain",
"=",
... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
train | images | Display a list of images with optional labels.
Args:
arrays: A list of NumPy arrays representing images
labels: A list of strings to label each image.
Defaults to show index if None
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None | lucid/misc/io/showing.py | def images(arrays, labels=None, domain=None, w=None):
"""Display a list of images with optional labels.
Args:
arrays: A list of NumPy arrays representing images
labels: A list of strings to label each image.
Defaults to show index if None
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None
"""
s = '<div style="display: flex; flex-direction: row;">'
for i, array in enumerate(arrays):
url = _image_url(array)
label = labels[i] if labels is not None else i
s += """<div style="margin-right:10px;">
{label}<br/>
<img src="{url}" style="margin-top:4px;">
</div>""".format(label=label, url=url)
s += "</div>"
_display_html(s) | def images(arrays, labels=None, domain=None, w=None):
"""Display a list of images with optional labels.
Args:
arrays: A list of NumPy arrays representing images
labels: A list of strings to label each image.
Defaults to show index if None
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None
"""
s = '<div style="display: flex; flex-direction: row;">'
for i, array in enumerate(arrays):
url = _image_url(array)
label = labels[i] if labels is not None else i
s += """<div style="margin-right:10px;">
{label}<br/>
<img src="{url}" style="margin-top:4px;">
</div>""".format(label=label, url=url)
s += "</div>"
_display_html(s) | [
"Display",
"a",
"list",
"of",
"images",
"with",
"optional",
"labels",
"."
] | tensorflow/lucid | python | https://github.com/tensorflow/lucid/blob/d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e/lucid/misc/io/showing.py#L78-L99 | [
"def",
"images",
"(",
"arrays",
",",
"labels",
"=",
"None",
",",
"domain",
"=",
"None",
",",
"w",
"=",
"None",
")",
":",
"s",
"=",
"'<div style=\"display: flex; flex-direction: row;\">'",
"for",
"i",
",",
"array",
"in",
"enumerate",
"(",
"arrays",
")",
":"... | d1a1e2e4fd4be61b89b8cba20dc425a5ae34576e |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.