index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
47,263 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/sheets_conditional_formatting.py | 23,24 | 1,112 | conditional_formatting | 30,108 | 24,108 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("sheets", "v4", credentials=creds)
my_range = {
"sheetId": 0,
"startRowIndex": 1,
"endRowIndex": 11,
"startColumnIndex": 0,
"endColumnIndex": 4,
}
requests = [
{... | """
Creates the batch_update the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 135 | 352 | 641 | 993 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,264 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/sheets_create.py | 23,24 | 1,51 | create | 30,47 | 24,47 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("sheets", "v4", credentials=creds)
spreadsheet = {"properties": {"title": title}}
spreadsheet = (
service.spreadsheets()
.create(body=spreadsheet, fields="spreadsheetId")
.execute()
)
... | """
Creates the Sheet the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
s... | 41 | 300 | 188 | 488 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,265 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/sheets_filter_views.py | 23,24 | 1,120 | filter_views | 30,116 | 24,116 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("sheets", "v4", credentials=creds)
my_range = {
"sheetId": 0,
"startRowIndex": 0,
"startColumnIndex": 0,
}
addfilterviewrequest = {
"addFilterView": {
"filter": {
... | """
Creates the batch_update the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 155 | 346 | 765 | 1,111 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,266 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/sheets_get_values.py | 23,24 | 1,53 | get_values | 30,49 | 24,49 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("sheets", "v4", credentials=creds)
result = (
service.spreadsheets()
.values()
.get(spreadsheetId=spreadsheet_id, range=range_name)
.execute()
)
rows = result.get("values", [])
... | """
Creates the batch_update the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 41 | 362 | 177 | 539 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,267 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/sheets_pivot_tables.py | 23,24 | 1,115 | pivot_tables | 30,111 | 24,111 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("sheets", "v4", credentials=creds)
# Create two sheets for our pivot table.
body = {"requests": [{"addSheet": {}}, {"addSheet": {}}]}
batch_update_response = (
service.spreadsheets()
.batchUpdat... | """
Creates the batch_update the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 150 | 350 | 715 | 1,065 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,268 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/sheets_update_values.py | 23,24 | 1,71 | update_values | 30,62 | 24,62 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("sheets", "v4", credentials=creds)
values = [
[
# Cell values ...
],
# Additional rows ...
]
# [START_EXCLUDE silent]
values = _values
# [END_EXCLUDE]
body = {"va... | """
Creates the batch_update the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 67 | 414 | 269 | 683 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,269 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_append_values.py | 23,24 | 20,39 | test_append_values | Testappendvalues | 25,39 | 24,39 | class Testappendvalues(BaseTest):
"""Unit test for append value Sheet snippet"""
def test_append_values(self):
FILL_FUNC_BODY | class Testappendvalues(BaseTest):
"""Unit test for append value Sheet snippet"""
def test_append_values(self):
"""test append values function"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
result = sheets_append_values.append_values(
spreadsheet_id, "Sheet1", "USER_ENTERED", [["A", "B"], ["C", "D"]]
)
self.assertIsNotNone(result)
self.assertEqual("Sheet1!A1:J10", result.get("tableRange"))
... | """test append values function"""
spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
result = sheets_append_values.append_values(
spreadsheet_id, "Sheet1", "USER_ENTERED", [["A", "B"], ["C", "D"]]
)
self.assertIsNotNone(result)
self.assertEqual("Shee... | 29 | 53 | 214 | 267 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,270 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_batch_get_values.py | 23,24 | 20,38 | test_batch_get_values | Testgetvalues | 25,38 | 24,38 | class Testgetvalues(BaseTest):
"""Unit test class for get value Sheet snippet"""
def test_batch_get_values(self):
FILL_FUNC_BODY | class Testgetvalues(BaseTest):
"""Unit test class for get value Sheet snippet"""
def test_batch_get_values(self):
"""test batch get values function"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
result = sheets_batch_get_values.batch_get_values(
spreadsheet_id, ["A1:A3", "B1:C1"]
)
self.assertIsNotNone(result)
valueranges = result.get("valueRanges")
self.assertIsNotNone(valueranges)
self... | """test batch get values function"""
spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
result = sheets_batch_get_values.batch_get_values(
spreadsheet_id, ["A1:A3", "B1:C1"]
)
self.assertIsNotNone(result)
valueranges = result.get("valueRanges")
s... | 23 | 57 | 167 | 224 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,271 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_batch_update.py | 24,25 | 21,40 | test_batch_update | Testbatchupdate | 26,40 | 25,40 | class Testbatchupdate(BaseTest):
"""Unit test class for Batch update Sheet snippet"""
def test_batch_update(self):
FILL_FUNC_BODY | class Testbatchupdate(BaseTest):
"""Unit test class for Batch update Sheet snippet"""
def test_batch_update(self):
"""test_batch_update function"""
FILL_FUNC_BODY | spreadsheet_id = sheets_create.create("Title")
self.populate_values(spreadsheet_id)
response = sheets_batch_update.sheets_batch_update(
spreadsheet_id, "New Title", "Hello", "Goodbye"
)
self.assertIsNotNone(response)
replies = response.get("replies")
self.assertIsNotNone(replies)
... | """test_batch_update function"""
spreadsheet_id = sheets_create.create("Title")
self.populate_values(spreadsheet_id)
response = sheets_batch_update.sheets_batch_update(
spreadsheet_id, "New Title", "Hello", "Goodbye"
)
self.assertIsNotNone(response)
replies = response.get("replies")
... | 26 | 57 | 189 | 246 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,272 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_batch_update_values.py | 23,24 | 20,36 | test_batch_update_values | Testbatchupdatevalues | 25,36 | 24,36 | class Testbatchupdatevalues(BaseTest):
"""Unit test for Batch update value Sheet snippet"""
def test_batch_update_values(self):
FILL_FUNC_BODY | class Testbatchupdatevalues(BaseTest):
"""Unit test for Batch update value Sheet snippet"""
def test_batch_update_values(self):
"""batch updates values"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
result = sheets_batch_update_values.batch_update_values(
spreadsheet_id, "A1:B2", "USER_ENTERED", [["A", "B"], ["C", "D"]]
)
self.assertIsNotNone(result)
self.assertEqual(1, len(result.get("responses")))
self.assertEqual(2, result.get("tota... | """batch updates values"""
spreadsheet_id = self.create_test_spreadsheet()
result = sheets_batch_update_values.batch_update_values(
spreadsheet_id, "A1:B2", "USER_ENTERED", [["A", "B"], ["C", "D"]]
)
self.assertIsNotNone(result)
self.assertEqual(1, len(result.get("responses")))
self.... | 23 | 57 | 164 | 221 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,273 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_conditional_formatting.py | 23,24 | 20,33 | test_conditional_formatting | Testconditionalformatting | 25,33 | 24,33 | class Testconditionalformatting(BaseTest):
"""Unit test for sheets conditional_formatting value Sheet snippet"""
def test_conditional_formatting(self):
FILL_FUNC_BODY | class Testconditionalformatting(BaseTest):
"""Unit test for sheets conditional_formatting value Sheet snippet"""
def test_conditional_formatting(self):
"""sheets_conditional_formatting function"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
response = sheets_conditional_formatting.conditional_formatting(
spreadsheet_id
)
self.assertEqual(2, len(response.get("replies")))
| """sheets_conditional_formatting function"""
spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
response = sheets_conditional_formatting.conditional_formatting(
spreadsheet_id
)
self.assertEqual(2, len(response.get("replies")))
| 11 | 66 | 87 | 153 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,274 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_create.py | 23,24 | 20,30 | test_create | Testsheetscreate | 25,30 | 24,30 | class Testsheetscreate(BaseTest):
"""Unit test class for Create Sheet snippet"""
def test_create(self):
FILL_FUNC_BODY | class Testsheetscreate(BaseTest):
"""Unit test class for Create Sheet snippet"""
def test_create(self):
"""sheet function for Create sheet"""
FILL_FUNC_BODY | spreadsheet_id = sheets_create.create("Title")
self.assertIsNotNone(spreadsheet_id)
self.delete_file_on_cleanup(spreadsheet_id)
| """sheet function for Create sheet"""
spreadsheet_id = sheets_create.create("Title")
self.assertIsNotNone(spreadsheet_id)
self.delete_file_on_cleanup(spreadsheet_id)
| 5 | 53 | 56 | 109 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,275 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_filter_views.py | 23,24 | 20,30 | test_filter_views | Testfilterviews | 25,30 | 24,30 | class Testfilterviews(BaseTest):
"""Unit test for sheets conditional_formatting value Sheet snippet"""
def test_filter_views(self):
FILL_FUNC_BODY | class Testfilterviews(BaseTest):
"""Unit test for sheets conditional_formatting value Sheet snippet"""
def test_filter_views(self):
"""test filter view function"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
sheets_filter_views.filter_views(spreadsheet_id)
| """test filter view function"""
spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
sheets_filter_views.filter_views(spreadsheet_id)
| 5 | 57 | 57 | 114 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,276 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_get_values.py | 23,24 | 20,35 | test_get_values | Testgetvalues | 25,35 | 24,35 | class Testgetvalues(BaseTest):
"""Unit test class for get value Sheet snippet"""
def test_get_values(self):
FILL_FUNC_BODY | class Testgetvalues(BaseTest):
"""Unit test class for get value Sheet snippet"""
def test_get_values(self):
"""test_get_values"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
result = sheets_get_values.get_values(spreadsheet_id, "A1:C2")
self.assertIsNotNone(result)
values = result.get("values")
self.assertIsNotNone(values)
self.assertEqual(2, len(values))
self.assertEqual(3,... | """test_get_values"""
spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
result = sheets_get_values.get_values(spreadsheet_id, "A1:C2")
self.assertIsNotNone(result)
values = result.get("values")
self.assertIsNotNone(values)
self.assertEqual(2, len(values... | 17 | 55 | 129 | 184 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,277 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_pivot_tables.py | 23,24 | 20,31 | test_pivot_tables | Testpivottables | 25,31 | 24,31 | class Testpivottables(BaseTest):
"""Unit test for Pivot tables value Sheet snippet"""
def test_pivot_tables(self):
FILL_FUNC_BODY | class Testpivottables(BaseTest):
"""Unit test for Pivot tables value Sheet snippet"""
def test_pivot_tables(self):
"""pivot table function"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
response = sheets_pivot_tables.pivot_tables(spreadsheet_id)
self.assertIsNotNone(response)
| """pivot table function"""
spreadsheet_id = self.create_test_spreadsheet()
self.populate_values(spreadsheet_id)
response = sheets_pivot_tables.pivot_tables(spreadsheet_id)
self.assertIsNotNone(response)
| 8 | 58 | 73 | 131 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,278 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/sheets/snippets/test_sheets_update_values.py | 23,24 | 20,35 | test_update_values | Testupdatesvalues | 25,35 | 24,35 | class Testupdatesvalues(BaseTest):
"""Unit test for update value Sheet snippet"""
def test_update_values(self):
FILL_FUNC_BODY | class Testupdatesvalues(BaseTest):
"""Unit test for update value Sheet snippet"""
def test_update_values(self):
"""test updates_values"""
FILL_FUNC_BODY | spreadsheet_id = self.create_test_spreadsheet()
result = sheets_update_values.update_values(
spreadsheet_id, "A1:B2", "USER_ENTERED", [["A", "B"], ["C", "D"]]
)
self.assertIsNotNone(result)
self.assertEqual(2, result.get("updatedRows"))
self.assertEqual(2, result.get("updatedColumns"))
... | """test updates_values"""
spreadsheet_id = self.create_test_spreadsheet()
result = sheets_update_values.update_values(
spreadsheet_id, "A1:B2", "USER_ENTERED", [["A", "B"], ["C", "D"]]
)
self.assertIsNotNone(result)
self.assertEqual(2, result.get("updatedRows"))
self.assertEqual(2, r... | 21 | 53 | 138 | 191 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,279 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/quickstart/quickstart.py | 31,32 | 1,76 | main | 35,73 | 32,73 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("token.json"):
creds = Credentials.from_authorized_user_file("token.json", SCOPES)
# If there are no (valid) creden... | """Shows basic usage of the Slides API.
Prints the number of slides and elements in a sample presentation.
"""
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("tok... | 130 | 402 | 438 | 840 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | |
47,280 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/base_test.py | 54,55 | 24,170 | tearDown | BaseTest | 55,61 | 55,61 | class BaseTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.credentials = cls.create_credentials()
http = cls.credentials.authorize(httplib2.Http())
cls.credentials.refresh(http)
cls.service = build("slides", "v1", http=http)
cls.drive_service = build("drive", "v3", http=http)
... | class BaseTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.credentials = cls.create_credentials()
http = cls.credentials.authorize(httplib2.Http())
cls.credentials.refresh(http)
cls.service = build("slides", "v1", http=http)
cls.drive_service = build("drive", "v3", http=http)
... | for file_id in self.files_to_delete:
try:
self.drive_service.files().delete(fileId=file_id).execute()
except errors.HttpError:
print(f"Unable to delete file {file_id}", file=sys.stderr)
| for file_id in self.files_to_delete:
try:
self.drive_service.files().delete(fileId=file_id).execute()
except errors.HttpError:
print(f"Unable to delete file {file_id}", file=sys.stderr)
| 14 | 1,299 | 79 | 1,378 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
47,281 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_copy_presentation.py | 23,24 | 1,54 | copy_presentation | 30,50 | 24,50 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... |
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
drive_service = build("drive", "v3", credentials=creds)
body = {"name": copy_title}
drive_response = (
drive_service.files().copy(fileId=presentation_id, body=body).execute()
)
presentation_copy_id = drive_respons... | """
Creates the copy Presentation the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member... | 44 | 366 | 192 | 558 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,282 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_create_bulleted_text.py | 23,24 | 1,68 | create_bulleted_text | 30,60 | 24,60 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
slides_service = build("slides", "v1", credentials=creds)
# Add arrow-diamond-disc bullets to all text in the shape.
requests = [
{
"createParagraphBullets": {
"objectId": shape_id,
... | """
Run create_bulleted_text the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 76 | 385 | 288 | 673 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,283 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_create_image.py | 23,24 | 1,86 | create_image | 30,81 | 24,81 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... |
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("slides", "v1", credentials=creds)
# pylint: disable = invalid-name
IMAGE_URL = (
"https://www.google.com/images/branding/"
"googlelogo/2x/googlelogo_color_272x92dp.png"
)
# pylint: disable... | """
Creates images the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
ser... | 112 | 371 | 521 | 892 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,284 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_create_presentation.py | 23,24 | 1,54 | create_presentation | 30,48 | 24,48 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("slides", "v1", credentials=creds)
body = {"title": title}
presentation = service.presentations().create(body=body).execute()
print(
f"Created presentation with ID:{(presentation.get('presentationId'))... | """
Creates the Presentation the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try... | 41 | 315 | 163 | 478 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,285 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_create_sheets_chart.py | 23,26 | 1,91 | create_sheets_chart | 32,80 | 26,80 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
slides_service = build("slides", "v1", credentials=creds)
# Embed a Sheets chart (indicated by the spreadsheet_id and
# sheet_chart_id) onto a page in the presentation.
# Setting the linking mode as "LINKED" allows the
# c... | """
create_sheets_chart the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
... | 136 | 473 | 508 | 981 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,286 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_create_slide.py | 23,24 | 1,73 | create_slide | 30,68 | 24,68 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("slides", "v1", credentials=creds)
# Add a slide at index 1 using the predefined
# 'TITLE_AND_TWO_COLUMNS' layout and the ID page_id.
requests = [
{
"createSlide": {
"objectI... | """
Creates the Presentation the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.\n
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
t... | 105 | 374 | 384 | 758 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,287 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_create_textbox_with_text.py | 23,24 | 1,90 | create_textbox_with_text | 30,82 | 24,82 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("slides", "v1", credentials=creds)
# Create a new square textbox, using the supplied element ID.
element_id = "MyTextBox_10"
pt350 = {"magnitude": 350, "unit": "PT"}
requests = [
{
"crea... | """
Creates the textbox with text, the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member... | 129 | 401 | 509 | 910 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,288 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_image_merging.py | 23,24 | 1,116 | image_merging | 30,105 | 24,105 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
slides_service = build("slides", "v1", credentials=creds)
drive_service = build("drive", "v3", credentials=creds)
logo_url = image_url
customer_graphic_url = image_url
# Duplicate the template presentation using the Driv... | """image_merging require template_presentation_id,
image_url and customer_name
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: dis... | 170 | 438 | 696 | 1,134 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,289 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_refresh_sheets_chart.py | 23,24 | 1,58 | refresh_sheets_chart | 30,51 | 24,51 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
slides_service = build("slides", "v1", credentials=creds)
# Refresh an existing linked Sheets chart embedded in a presentation.
requests = [{"refreshSheetsChart": {"objectId": presentation_chart_id}}]
# Execute the request.
... | """
refresh_sheets_chart the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
... | 64 | 402 | 229 | 631 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,290 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_simple_text_replace.py | 23,24 | 1,73 | simple_text_replace | 30,64 | 24,64 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
slides_service = build("slides", "v1", credentials=creds)
# Remove existing text in the shape, then insert new text.
requests = []
requests.append(
{"deleteText": {"objectId": shape_id, "textRange": {"type": "ALL"}}}
... | """
Run simple_text_replace the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:... | 87 | 399 | 314 | 713 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,291 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_text_merging.py | 23,24 | 1,130 | text_merging | 30,121 | 24,121 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("slides", "v1", credentials=creds)
sheets_service = build("sheets", "v4", credentials=creds)
drive_service = build("drive", "v3", credentials=creds)
# Use the Sheets API to load data, one record per row.
d... | """
Run Text merging the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
s... | 244 | 428 | 889 | 1,317 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,292 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/slides_text_style_update.py | 23,24 | 1,110 | text_style_update | 30,103 | 24,103 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
service = build("slides", "v1", credentials=creds)
# Update the text style so that the first 5 characters are bolded
# and italicized, the next 5 are displayed in blue 14 pt Times
# New Roman font, and the next 5 are hyperlink... | """
create_sheets_chart the user has access to.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
# pylint: disable=maybe-no-member
try:
... | 179 | 388 | 675 | 1,063 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
dis... | |
47,293 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_copy_presentation.py | 23,24 | 20,33 | test_copy_presentation | TestCopyPresentation | 25,33 | 24,33 | class TestCopyPresentation(BaseTest):
"""Unit test for Copy presentation snippet"""
def test_copy_presentation(self):
FILL_FUNC_BODY | class TestCopyPresentation(BaseTest):
"""Unit test for Copy presentation snippet"""
def test_copy_presentation(self):
"""set title for copy presentation"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
copy_id = slides_copy_presentation.copy_presentation(
presentation_id, "My Duplicate Presentation"
)
self.assertIsNotNone(copy_id)
self.delete_file_on_cleanup(copy_id)
| """set title for copy presentation"""
presentation_id = self.create_test_presentation()
copy_id = slides_copy_presentation.copy_presentation(
presentation_id, "My Duplicate Presentation"
)
self.assertIsNotNone(copy_id)
self.delete_file_on_cleanup(copy_id)
| 13 | 55 | 89 | 144 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,294 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_create_bulleted_text.py | 24,25 | 21,35 | test_create_bulleted_text | TestCreateBulletedText | 26,35 | 25,35 | class TestCreateBulletedText(BaseTest):
"""Unit test for create_bulleted_text snippet"""
def test_create_bulleted_text(self):
FILL_FUNC_BODY | class TestCreateBulletedText(BaseTest):
"""Unit test for create_bulleted_text snippet"""
def test_create_bulleted_text(self):
"""create_bulleted_text function"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
box_id = self.create_test_textbox(presentation_id, page_id)
response = slides_create_bulleted_text.create_bulleted_text(
presentation_id, box_id
)
self.assertEqual(1, len(response.... | """create_bulleted_text function"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
box_id = self.create_test_textbox(presentation_id, page_id)
response = slides_create_bulleted_text.create_bulleted_text(
presentation_id, box_id
... | 21 | 64 | 136 | 200 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,295 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_create_image.py | 24,25 | 21,34 | test_create_image | TestCreateTextboxWithText | 26,34 | 25,34 | class TestCreateTextboxWithText(BaseTest):
"""Unit test case for create_image snippet"""
def test_create_image(self):
FILL_FUNC_BODY | class TestCreateTextboxWithText(BaseTest):
"""Unit test case for create_image snippet"""
def test_create_image(self):
"""presentation id and page id for create image"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
response = slides_create_image.create_image(presentation_id, page_id)
self.assertEqual(1, len(response.get("replies")), msg=pformat(response))
image_id = response.get("replies")[0].get("create... | """presentation id and page id for create image"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
response = slides_create_image.create_image(presentation_id, page_id)
self.assertEqual(1, len(response.get("replies")), msg=pformat(response)... | 20 | 61 | 148 | 209 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,296 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_create_presentation.py | 23,24 | 20,30 | test_create_presentation | TestCreatePresentation | 25,30 | 24,30 | class TestCreatePresentation(BaseTest):
"""Unit test for create presentation snippet"""
def test_create_presentation(self):
FILL_FUNC_BODY | class TestCreatePresentation(BaseTest):
"""Unit test for create presentation snippet"""
def test_create_presentation(self):
"""Set title for create presentation"""
FILL_FUNC_BODY | presentation = slides_create_presentation.create_presentation("Title")
self.assertIsNotNone(presentation)
self.delete_file_on_cleanup(presentation.get("presentationId"))
| """Set title for create presentation"""
presentation = slides_create_presentation.create_presentation("Title")
self.assertIsNotNone(presentation)
self.delete_file_on_cleanup(presentation.get("presentationId"))
| 5 | 55 | 59 | 114 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,297 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_create_sheets_chart.py | 27,28 | 21,41 | test_create_sheets_chart | TestCreateSheetsChart | 29,41 | 28,41 | class TestCreateSheetsChart(BaseTest):
"""Unit test for create_sheets_chart snippet"""
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
CHART_ID = 1107320627
def test_create_sheets_chart(self):
FILL_FUNC_BODY | class TestCreateSheetsChart(BaseTest):
"""Unit test for create_sheets_chart snippet"""
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
CHART_ID = 1107320627
def test_create_sheets_chart(self):
"""create_sheet chart method"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
response = slides_create_sheets_chart.create_sheets_chart(
presentation_id, page_id, self.DATA_SPREADSHEET_ID, self.CHART_ID
)
self.assertEqual(1, len(response.get("replies")), msg=pfo... | """create_sheet chart method"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
response = slides_create_sheets_chart.create_sheets_chart(
presentation_id, page_id, self.DATA_SPREADSHEET_ID, self.CHART_ID
)
self.assertEqual(1, l... | 26 | 132 | 188 | 320 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,298 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_create_slide.py | 23,24 | 20,35 | test_create_slide | TestCreateSlide | 25,35 | 24,35 | class TestCreateSlide(BaseTest):
"""Unit test for create Slide snippet"""
def test_create_slide(self):
FILL_FUNC_BODY | class TestCreateSlide(BaseTest):
"""Unit test for create Slide snippet"""
def test_create_slide(self):
"""pass presentation_id and page_id for creating the slides"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
self.add_slides(presentation_id, 3)
page_id = "my_page_id"
response = slides_create_slide.create_slide(presentation_id, page_id)
self.assertEqual(
page_id,
response.get("replies")[0].get("createSlide").get("objectId"),
)
| """pass presentation_id and page_id for creating the slides"""
presentation_id = self.create_test_presentation()
self.add_slides(presentation_id, 3)
page_id = "my_page_id"
response = slides_create_slide.create_slide(presentation_id, page_id)
self.assertEqual(
page_id,
response.ge... | 16 | 62 | 118 | 180 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,299 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_create_textbox_with_text.py | 24,25 | 21,36 | test_create_textbox_with_text | TestCreateTextboxWithText | 26,36 | 25,36 | class TestCreateTextboxWithText(BaseTest):
"""Unit test for TestCreateTextboxWithText snippet"""
def test_create_textbox_with_text(self):
FILL_FUNC_BODY | class TestCreateTextboxWithText(BaseTest):
"""Unit test for TestCreateTextboxWithText snippet"""
def test_create_textbox_with_text(self):
"""Pass Presentation id and page id"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
response = slides_create_textbox_with_text.create_textbox_with_text(
presentation_id, page_id
)
self.assertEqual(2, len(response.get("replies")), msg=pformat(response))
box_id = re... | """Pass Presentation id and page id"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
response = slides_create_textbox_with_text.create_textbox_with_text(
presentation_id, page_id
)
self.assertEqual(2, len(response.get("replies... | 22 | 66 | 163 | 229 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,300 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_image_merging.py | 30,31 | 21,47 | test_image_merging | TestTextMerging | 32,47 | 31,47 | class TestTextMerging(BaseTest):
"""Unit test for text merging snippet"""
TEMPLATE_PRESENTATION_ID = "10QnVUx1X2qHsL17WUidGpPh_SQhXYx40CgIxaKk8jU4"
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
IMAGE_URL = "https://picsum.photos/200"
CHART_ID = 1107320627
CUSTOMER_NAME = "Fake Custom... | class TestTextMerging(BaseTest):
"""Unit test for text merging snippet"""
TEMPLATE_PRESENTATION_ID = "10QnVUx1X2qHsL17WUidGpPh_SQhXYx40CgIxaKk8jU4"
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
IMAGE_URL = "https://picsum.photos/200"
CHART_ID = 1107320627
CUSTOMER_NAME = "Fake Custom... | response = slides_image_merging.image_merging(
self.TEMPLATE_PRESENTATION_ID, self.IMAGE_URL, self.CUSTOMER_NAME
)
presentation_id = response.get("presentationId")
self.delete_file_on_cleanup(presentation_id)
self.assertIsNotNone(presentation_id, msg=pformat(response))
self.assertEqual(2... | """image merging function"""
response = slides_image_merging.image_merging(
self.TEMPLATE_PRESENTATION_ID, self.IMAGE_URL, self.CUSTOMER_NAME
)
presentation_id = response.get("presentationId")
self.delete_file_on_cleanup(presentation_id)
self.assertIsNotNone(presentation_id, msg=pformat(... | 31 | 217 | 213 | 430 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,301 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_refresh_sheets_chart.py | 27,28 | 21,40 | test_refresh_sheets_chart | TestCreateSheetsChart | 29,40 | 28,40 | class TestCreateSheetsChart(BaseTest):
"""Unit test for refresh_sheets_chart snippet"""
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
CHART_ID = 1107320627
def test_refresh_sheets_chart(self):
FILL_FUNC_BODY | class TestCreateSheetsChart(BaseTest):
"""Unit test for refresh_sheets_chart snippet"""
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
CHART_ID = 1107320627
def test_refresh_sheets_chart(self):
"""refresh_sheets_chart method"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
chart_id = self.create_test_sheets_chart(
presentation_id, page_id, self.DATA_SPREADSHEET_ID, self.CHART_ID
)
response = slides_refresh_sheets_chart.refresh_sheets_chart(
prese... | """refresh_sheets_chart method"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
chart_id = self.create_test_sheets_chart(
presentation_id, page_id, self.DATA_SPREADSHEET_ID, self.CHART_ID
)
response = slides_refresh_sheets_cha... | 25 | 136 | 164 | 300 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,302 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_simple_text_replace.py | 24,25 | 21,35 | test_simple_text_replace | TestSimpleTextReplace | 26,35 | 25,35 | class TestSimpleTextReplace(BaseTest):
"""Unit test for SimpleTextReplace snippet"""
def test_simple_text_replace(self):
FILL_FUNC_BODY | class TestSimpleTextReplace(BaseTest):
"""Unit test for SimpleTextReplace snippet"""
def test_simple_text_replace(self):
"""test_simple_text_replace function"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
box_id = self.create_test_textbox(presentation_id, page_id)
response = slides_simple_text_replace.simple_text_replace(
presentation_id, box_id, "MY NEW TEXT"
)
self.assertEqual(2, ... | """test_simple_text_replace function"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
box_id = self.create_test_textbox(presentation_id, page_id)
response = slides_simple_text_replace.simple_text_replace(
presentation_id, box_id, ... | 24 | 59 | 141 | 200 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,303 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_text_merging.py | 26,27 | 20,33 | test_text_merging | TestTextMerging | 28,33 | 27,33 | class TestTextMerging(BaseTest):
"""Unit test for SimpleTextReplace snippet"""
TEMPLATE_PRESENTATION_ID = "10QnVUx1X2qHsL17WUidGpPh_SQhXYx40CgIxaKk8jU4"
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
def test_text_merging(self):
FILL_FUNC_BODY | class TestTextMerging(BaseTest):
"""Unit test for SimpleTextReplace snippet"""
TEMPLATE_PRESENTATION_ID = "10QnVUx1X2qHsL17WUidGpPh_SQhXYx40CgIxaKk8jU4"
DATA_SPREADSHEET_ID = "17eqFZl_WK4WVixX8PjvjfLD77DraoFwMDXeiHB3dvuM"
def test_text_merging(self):
"""text_merging method"""
FILL_FUNC_BODY | slides_text_merging.text_merging(
self.TEMPLATE_PRESENTATION_ID, self.DATA_SPREADSHEET_ID
)
| """text_merging method"""
slides_text_merging.text_merging(
self.TEMPLATE_PRESENTATION_ID, self.DATA_SPREADSHEET_ID
)
| 4 | 166 | 51 | 217 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,304 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/slides/snippets/test_slides_text_style_update.py | 24,25 | 21,35 | test_text_style_update | TestTextStyleUpdate | 26,35 | 25,35 | class TestTextStyleUpdate(BaseTest):
"""Unit test for SimpleTextReplace snippet"""
def test_text_style_update(self):
FILL_FUNC_BODY | class TestTextStyleUpdate(BaseTest):
"""Unit test for SimpleTextReplace snippet"""
def test_text_style_update(self):
"""test_text_style_update function"""
FILL_FUNC_BODY | presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
box_id = self.create_test_textbox(presentation_id, page_id)
response = slides_text_style_update.text_style_update(
presentation_id, box_id
)
self.assertEqual(3, len(response.get("r... | """test_text_style_update function"""
presentation_id = self.create_test_presentation()
page_id = self.add_slides(presentation_id, 1, "BLANK")[0]
box_id = self.create_test_textbox(presentation_id, page_id)
response = slides_text_style_update.text_style_update(
presentation_id, box_id
)
... | 21 | 59 | 134 | 193 | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... | """
Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distri... |
47,305 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/tasks/quickstart/quickstart.py | 28,29 | 1,72 | main | 32,69 | 29,69 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("token.json"):
creds = Credentials.from_authorized_user_file("token.json", SCOPES)
# If there are no (valid) creden... | """Shows basic usage of the Tasks API.
Prints the title and ID of the first 10 task lists.
"""
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("token.json"):
c... | 124 | 346 | 412 | 758 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | |
47,306 | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples | /home/hieuvd/dung_repo/raw/stars_03/googleworkspace_python-samples/python-samples/vault/quickstart/quickstart.py | 28,29 | 1,72 | main | 32,69 | 29,69 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("token.json"):
creds = Credentials.from_authorized_user_file("token.json", SCOPES)
# If there are no (valid) creden... | """Shows basic usage of the Vault API.
Prints the names and IDs of the first 10 matters in Vault.
"""
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists("token.json")... | 122 | 347 | 416 | 763 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | |
47,658 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/dataset/reseed.py | 4,5 | 1,7 | reseed | 5,7 | 5,7 | import torch
import random
def reseed(seed):
FILL_FUNC_BODY | import torch
import random
def reseed(seed):
FILL_FUNC_BODY | random.seed(seed)
torch.manual_seed(seed) | random.seed(seed)
torch.manual_seed(seed) | 2 | 25 | 21 | 46 | import torch
import random
def reseed(seed):
FILL_FUNC_BODY | import torch
import random
def reseed(seed):
FILL_FUNC_BODY | |
47,659 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/dataset/util.py | 4,5 | 1,14 | all_to_onehot | 5,14 | 5,14 | import numpy as np
def all_to_onehot(masks, labels):
FILL_FUNC_BODY | import numpy as np
def all_to_onehot(masks, labels):
FILL_FUNC_BODY | if len(masks.shape) == 3:
Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)
else:
Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)
for ni, l in enumerate(labels):
Ms[ni] = (masks == l).astype(np.uint8)
... | if len(masks.shape) == 3:
Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)
else:
Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)
for ni, l in enumerate(labels):
Ms[ni] = (masks == l).astype(np.uint8)
... | 30 | 32 | 145 | 177 | import numpy as np
def all_to_onehot(masks, labels):
FILL_FUNC_BODY | import numpy as np
def all_to_onehot(masks, labels):
FILL_FUNC_BODY | |
47,660 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/data/mask_mapper.py | 26,27 | 7,65 | convert_mask | MaskMapper | 27,56 | 27,56 | class MaskMapper:
"""
This class is used to convert a indexed-mask to a one-hot representation.
It also takes care of remapping non-continuous indices
It has two modes:
1. Default. Only masks with new indices are supposed to go into the remapper.
This is also the case for YouTubeVOS.
... | class MaskMapper:
"""
This class is used to convert a indexed-mask to a one-hot representation.
It also takes care of remapping non-continuous indices
It has two modes:
1. Default. Only masks with new indices are supposed to go into the remapper.
This is also the case for YouTubeVOS.
... | # mask is in index representation, H*W numpy array
labels = np.unique(mask).astype(np.uint8)
labels = labels[labels!=0].tolist()
new_labels = list(set(labels) - set(self.labels))
if not exhaustive:
assert len(new_labels) == len(labels), 'Old labels found in non-exhau... | # mask is in index representation, H*W numpy array
labels = np.unique(mask).astype(np.uint8)
labels = labels[labels!=0].tolist()
new_labels = list(set(labels) - set(self.labels))
if not exhaustive:
assert len(new_labels) == len(labels), 'Old labels found in non-exhau... | 82 | 301 | 369 | 670 | import numpy as np
import torch
from dataset.util import all_to_onehot
class MaskMapper:
"""
This class is used to convert a indexed-mask to a one-hot representation.
It also takes care of remapping non-continuous indices
It has two modes:
1. Default. Only masks with new indices are supposed ... | import numpy as np
import torch
from dataset.util import all_to_onehot
class MaskMapper:
"""
This class is used to convert a indexed-mask to a one-hot representation.
It also takes care of remapping non-continuous indices
It has two modes:
1. Default. Only masks with new indices are supposed ... |
47,661 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/data/test_datasets.py | 50,51 | 31,63 | get_datasets | DAVISTestDataset | 51,59 | 51,59 | class DAVISTestDataset:
def __init__(self, data_root, imset='2017/val.txt', size=-1):
if size != 480:
self.image_dir = path.join(data_root, 'JPEGImages', 'Full-Resolution')
self.mask_dir = path.join(data_root, 'Annotations', 'Full-Resolution')
if not path.exists(self.imag... | class DAVISTestDataset:
def __init__(self, data_root, imset='2017/val.txt', size=-1):
if size != 480:
self.image_dir = path.join(data_root, 'JPEGImages', 'Full-Resolution')
self.mask_dir = path.join(data_root, 'Annotations', 'Full-Resolution')
if not path.exists(self.imag... | for video in self.vid_list:
yield VideoReader(video,
path.join(self.image_dir, video),
path.join(self.mask_dir, video),
size=self.size,
size_dir=path.join(self.size_dir, video),
)
| for video in self.vid_list:
yield VideoReader(video,
path.join(self.image_dir, video),
path.join(self.mask_dir, video),
size=self.size,
size_dir=path.join(self.size_dir, video),
)
| 14 | 393 | 87 | 480 | import os
from os import path
import json
from inference.data.video_reader import VideoReader
class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
se... | import os
from os import path
import json
from inference.data.video_reader import VideoReader
class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
se... |
47,662 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/data/test_datasets.py | 16,17 | 8,31 | get_datasets | LongTestDataset | 17,27 | 17,27 | class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
self.vid_list = sorted(os.listdir(self.image_dir))
def get_datasets(self):
FILL_FUNC_BODY d... | class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
self.vid_list = sorted(os.listdir(self.image_dir))
def get_datasets(self):
FILL_FUNC_BODY d... | for video in self.vid_list:
yield VideoReader(video,
path.join(self.image_dir, video),
path.join(self.mask_dir, video),
to_save = [
name[:-4] for name in os.listdir(path.join(self.mask_dir, video))
],
... | for video in self.vid_list:
yield VideoReader(video,
path.join(self.image_dir, video),
path.join(self.mask_dir, video),
to_save = [
name[:-4] for name in os.listdir(path.join(self.mask_dir, video))
],
... | 22 | 138 | 108 | 246 | import os
from os import path
import json
from inference.data.video_reader import VideoReader
class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
se... | import os
from os import path
import json
from inference.data.video_reader import VideoReader
class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
se... |
47,663 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/data/test_datasets.py | 85,86 | 63,97 | get_datasets | YouTubeVOSTestDataset | 86,95 | 86,95 | class YouTubeVOSTestDataset:
def __init__(self, data_root, split, size=480):
self.image_dir = path.join(data_root, 'all_frames', split+'_all_frames', 'JPEGImages')
self.mask_dir = path.join(data_root, split, 'Annotations')
self.size = size
self.vid_list = sorted(os.listdir(self.imag... | class YouTubeVOSTestDataset:
def __init__(self, data_root, split, size=480):
self.image_dir = path.join(data_root, 'all_frames', split+'_all_frames', 'JPEGImages')
self.mask_dir = path.join(data_root, split, 'Annotations')
self.size = size
self.vid_list = sorted(os.listdir(self.imag... | for video in self.vid_list:
yield VideoReader(video,
path.join(self.image_dir, video),
path.join(self.mask_dir, video),
size=self.size,
to_save=self.req_frame_list[video],
use_all_mask=True
)
| for video in self.vid_list:
yield VideoReader(video,
path.join(self.image_dir, video),
path.join(self.mask_dir, video),
size=self.size,
to_save=self.req_frame_list[video],
use_all_mask=True
)
| 14 | 302 | 96 | 398 | import os
from os import path
import json
from inference.data.video_reader import VideoReader
class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
se... | import os
from os import path
import json
from inference.data.video_reader import VideoReader
class LongTestDataset:
def __init__(self, data_root, size=-1):
self.image_dir = path.join(data_root, 'JPEGImages')
self.mask_dir = path.join(data_root, 'Annotations')
self.size = size
se... |
47,664 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/data/video_reader.py | 89,90 | 14,101 | resize_mask | VideoReader | 90,96 | 90,96 | class VideoReader(Dataset):
"""
This class is used to read a video, one frame at a time
"""
def __init__(self, vid_name, image_dir, mask_dir, size=-1, to_save=None, use_all_mask=False, size_dir=None):
"""
image_dir - points to a directory of jpg images
mask_dir - points to a dire... | class VideoReader(Dataset):
"""
This class is used to read a video, one frame at a time
"""
def __init__(self, vid_name, image_dir, mask_dir, size=-1, to_save=None, use_all_mask=False, size_dir=None):
"""
image_dir - points to a directory of jpg images
mask_dir - points to a dire... | # mask transform is applied AFTER mapper, so we need to post-process it in eval.py
h, w = mask.shape[-2:]
min_hw = min(h, w)
return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)),
mode='nearest')
| # mask transform is applied AFTER mapper, so we need to post-process it in eval.py
h, w = mask.shape[-2:]
min_hw = min(h, w)
return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)),
mode='nearest')
| 28 | 910 | 101 | 1,011 | import os
from os import path
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from torchvision.transforms import InterpolationMode
import torch.nn.functional as F
from PIL import Image
import numpy as np
from dataset.range_transform import im_normalization
class VideoReader(Dataset):... | import os
from os import path
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from torchvision.transforms import InterpolationMode
import torch.nn.functional as F
from PIL import Image
import numpy as np
from dataset.range_transform import im_normalization
class VideoReader(Dataset):... |
47,665 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/inference_core.py | 22,23 | 8,108 | clear_memory | InferenceCore | 23,29 | 23,29 | class InferenceCore:
def __init__(self, network:XMem, config):
self.config = config
self.network = network
self.mem_every = config['mem_every']
self.deep_update_every = config['deep_update_every']
self.enable_long_term = config['enable_long_term']
# if deep_update_ev... | class InferenceCore:
def __init__(self, network:XMem, config):
self.config = config
self.network = network
self.mem_every = config['mem_every']
self.deep_update_every = config['deep_update_every']
self.enable_long_term = config['enable_long_term']
# if deep_update_ev... | self.curr_ti = -1
self.last_mem_ti = 0
if not self.deep_update_sync:
self.last_deep_update_ti = -self.deep_update_every
self.memory = MemoryManager(config=self.config)
| self.curr_ti = -1
self.last_mem_ti = 0
if not self.deep_update_sync:
self.last_deep_update_ti = -self.deep_update_every
self.memory = MemoryManager(config=self.config)
| 15 | 1,399 | 76 | 1,475 | from inference.memory_manager import MemoryManager
from model.network import XMem
from model.aggregate import aggregate
from util.tensor_util import pad_divide_by, unpad
class InferenceCore:
def __init__(self, network:XMem, config):
self.config = config
self.network = network
self.mem_eve... | from inference.memory_manager import MemoryManager
from model.network import XMem
from model.aggregate import aggregate
from util.tensor_util import pad_divide_by, unpad
class InferenceCore:
def __init__(self, network:XMem, config):
self.config = config
self.network = network
self.mem_eve... |
47,666 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/controller.py | 27,28 | 11,111 | set_image | InteractiveController | 28,33 | 28,33 | class InteractiveController:
def __init__(self, net, device, predictor_params, prob_thresh=0.5):
self.net = net.to(device)
self.prob_thresh = prob_thresh
self.clicker = clicker.Clicker()
self.states = []
self.probs_history = []
self.object_count = 0
self._resu... | class InteractiveController:
def __init__(self, net, device, predictor_params, prob_thresh=0.5):
self.net = net.to(device)
self.prob_thresh = prob_thresh
self.clicker = clicker.Clicker()
self.states = []
self.probs_history = []
self.object_count = 0
self._resu... | self.image = image
self._result_mask = torch.zeros(image.shape[-2:], dtype=torch.uint8)
self.object_count = 0
self.reset_last_object()
| self.image = image
self._result_mask = torch.zeros(image.shape[-2:], dtype=torch.uint8)
self.object_count = 0
self.reset_last_object()
| 11 | 957 | 62 | 1,019 | import torch
try:
from torch import mps
except:
pass
from ..fbrs.inference import clicker
from ..fbrs.inference.predictors import get_predictor
class InteractiveController:
def __init__(self, net, device, predictor_params, prob_thresh=0.5):
self.net = net.to(device)
self.prob_thresh = pro... | import torch
try:
from torch import mps
except:
pass
from ..fbrs.inference import clicker
from ..fbrs.inference.predictors import get_predictor
class InteractiveController:
def __init__(self, net, device, predictor_params, prob_thresh=0.5):
self.net = net.to(device)
self.prob_thresh = pro... |
47,667 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/evaluation.py | 16,17 | 1,57 | evaluate_dataset | 17,36 | 17,36 | from time import time
import numpy as np
import torch
from ..inference import utils
from ..inference.clicker import Clicker
try:
get_ipython()
from tqdm import tqdm_notebook as tqdm
except NameError:
from tqdm import tqdm
def evaluate_dataset(dataset, predictor, oracle_eval=False, **kwargs):
FILL_FUNC_... | from time import time
import numpy as np
import torch
from ..inference import utils
from ..inference.clicker import Clicker
try:
get_ipython()
from tqdm import tqdm_notebook as tqdm
except NameError:
from tqdm import tqdm
def evaluate_dataset(dataset, predictor, oracle_eval=False, **kwargs):
FILL_FUNC_... | all_ious = []
start_time = time()
for index in tqdm(range(len(dataset)), leave=False):
sample = dataset.get_sample(index)
item = dataset[index]
if oracle_eval:
gt_mask = torch.tensor(sample['instances_mask'], dtype=torch.float32)
gt_mask = gt_mask.unsqueeze(... | all_ious = []
start_time = time()
for index in tqdm(range(len(dataset)), leave=False):
sample = dataset.get_sample(index)
item = dataset[index]
if oracle_eval:
gt_mask = torch.tensor(sample['instances_mask'], dtype=torch.float32)
gt_mask = gt_mask.unsqueeze(... | 47 | 370 | 239 | 609 | from time import time
import numpy as np
import torch
from ..inference import utils
from ..inference.clicker import Clicker
try:
get_ipython()
from tqdm import tqdm_notebook as tqdm
except NameError:
from tqdm import tqdm
def evaluate_dataset(dataset, predictor, oracle_eval=False, **kwargs):
FILL_FUNC_... | from time import time
import numpy as np
import torch
from ..inference import utils
from ..inference.clicker import Clicker
try:
get_ipython()
from tqdm import tqdm_notebook as tqdm
except NameError:
from tqdm import tqdm
def evaluate_dataset(dataset, predictor, oracle_eval=False, **kwargs):
FILL_FUNC_... | |
47,668 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/predictors/base.py | 76,77 | 7,101 | get_points_nd | BasePredictor | 77,96 | 77,96 | class BasePredictor(object):
def __init__(self, net, device,
net_clicks_limit=None,
with_flip=False,
zoom_in=None,
max_size=None,
**kwargs):
self.net = net
self.with_flip = with_flip
self.net_clicks_limit = ... | class BasePredictor(object):
def __init__(self, net, device,
net_clicks_limit=None,
with_flip=False,
zoom_in=None,
max_size=None,
**kwargs):
self.net = net
self.with_flip = with_flip
self.net_clicks_limit = ... | total_clicks = []
num_pos_clicks = [sum(x.is_positive for x in clicks_list) for clicks_list in clicks_lists]
num_neg_clicks = [len(clicks_list) - num_pos for clicks_list, num_pos in zip(clicks_lists, num_pos_clicks)]
num_max_points = max(num_pos_clicks + num_neg_clicks)
if self.n... | total_clicks = []
num_pos_clicks = [sum(x.is_positive for x in clicks_list) for clicks_list in clicks_lists]
num_neg_clicks = [len(clicks_list) - num_pos for clicks_list, num_pos in zip(clicks_lists, num_pos_clicks)]
num_max_points = max(num_pos_clicks + num_neg_clicks)
if self.n... | 95 | 874 | 387 | 1,261 | import torch
import torch.nn.functional as F
from ..transforms import AddHorizontalFlip, SigmoidForPred, LimitLongestSide
class BasePredictor(object):
def __init__(self, net, device,
net_clicks_limit=None,
with_flip=False,
zoom_in=None,
max_size... | import torch
import torch.nn.functional as F
from ..transforms import AddHorizontalFlip, SigmoidForPred, LimitLongestSide
class BasePredictor(object):
def __init__(self, net, device,
net_clicks_limit=None,
with_flip=False,
zoom_in=None,
max_size... |
47,669 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/predictors/brs_functors.py | 33,34 | 8,83 | init_click | BaseOptimizer | 34,41 | 34,41 | class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),
with_flip=False,
flip_average=False,
**kwargs):
self.... | class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),
with_flip=False,
flip_average=False,
**kwargs):
self.... | self.best_prediction = None
self._get_prediction_logits = get_prediction_logits
self._click_masks = (pos_mask, neg_mask)
self._opt_shape = shape
self._last_mask = None
self.device = device
| self.best_prediction = None
self._get_prediction_logits = get_prediction_logits
self._click_masks = (pos_mask, neg_mask)
self._opt_shape = shape
self._last_mask = None
self.device = device
| 19 | 903 | 80 | 983 | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),... | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),... |
47,670 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/predictors/brs_functors.py | 84,85 | 83,94 | unpack_opt_params | InputOptimizer | 85,94 | 85,94 | class InputOptimizer(BaseOptimizer):
def unpack_opt_params(self, opt_params):
FILL_FUNC_BODY | class InputOptimizer(BaseOptimizer):
def unpack_opt_params(self, opt_params):
FILL_FUNC_BODY | opt_params = opt_params.view(self._opt_shape)
if self.with_flip:
opt_params_flipped = torch.flip(opt_params, dims=[3])
opt_params = torch.cat([opt_params, opt_params_flipped], dim=0)
reg_loss = self.reg_weight * torch.sum(opt_params**2)
return (opt_params,), reg_... | opt_params = opt_params.view(self._opt_shape)
if self.with_flip:
opt_params_flipped = torch.flip(opt_params, dims=[3])
opt_params = torch.cat([opt_params, opt_params_flipped], dim=0)
reg_loss = self.reg_weight * torch.sum(opt_params**2)
return (opt_params,), reg_... | 22 | 37 | 126 | 163 | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),... | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),... |
47,671 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/predictors/brs_functors.py | 100,101 | 94,110 | unpack_opt_params | ScaleBiasOptimizer | 101,110 | 101,110 | class ScaleBiasOptimizer(BaseOptimizer):
def __init__(self, *args, scale_act=None, reg_bias_weight=10.0, **kwargs):
super().__init__(*args, **kwargs)
self.scale_act = scale_act
self.reg_bias_weight = reg_bias_weight
def unpack_opt_params(self, opt_params):
FILL_FUNC_BODY | class ScaleBiasOptimizer(BaseOptimizer):
def __init__(self, *args, scale_act=None, reg_bias_weight=10.0, **kwargs):
super().__init__(*args, **kwargs)
self.scale_act = scale_act
self.reg_bias_weight = reg_bias_weight
def unpack_opt_params(self, opt_params):
FILL_FUNC_BODY | scale, bias = torch.chunk(opt_params, 2, dim=0)
reg_loss = self.reg_weight * (torch.sum(scale**2) + self.reg_bias_weight * torch.sum(bias**2))
if self.scale_act == 'tanh':
scale = torch.tanh(scale)
elif self.scale_act == 'sin':
scale = torch.sin(scale)
r... | scale, bias = torch.chunk(opt_params, 2, dim=0)
reg_loss = self.reg_weight * (torch.sum(scale**2) + self.reg_bias_weight * torch.sum(bias**2))
if self.scale_act == 'tanh':
scale = torch.tanh(scale)
elif self.scale_act == 'sin':
scale = torch.sin(scale)
r... | 35 | 114 | 137 | 251 | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),... | import torch
import numpy as np
from ...model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(self, optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),... |
47,672 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/predictors/brs_losses.py | 11,12 | 6,29 | forward | BRSMaskLoss | 12,29 | 12,29 | class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
FILL_FUNC_BODY | class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
FILL_FUNC_BODY | pos_diff = (1 - result) * pos_mask
pos_target = torch.sum(pos_diff ** 2)
pos_target = pos_target / (torch.sum(pos_mask) + self._eps)
neg_diff = result * neg_mask
neg_target = torch.sum(neg_diff ** 2)
neg_target = neg_target / (torch.sum(neg_mask) + self._eps)
... | pos_diff = (1 - result) * pos_mask
pos_target = torch.sum(pos_diff ** 2)
pos_target = pos_target / (torch.sum(pos_mask) + self._eps)
neg_diff = result * neg_mask
neg_target = torch.sum(neg_diff ** 2)
neg_target = neg_target / (torch.sum(neg_mask) + self._eps)
... | 53 | 75 | 221 | 296 | import torch
from ...model.losses import SigmoidBinaryCrossEntropyLoss
class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
FILL_FUNC_BODYclass OracleMaskLoss(torch.nn.Module):
def __init__(sel... | import torch
from ...model.losses import SigmoidBinaryCrossEntropyLoss
class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
FILL_FUNC_BODYclass OracleMaskLoss(torch.nn.Module):
def __init__(sel... |
47,673 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/predictors/brs_losses.py | 41,42 | 29,59 | forward | OracleMaskLoss | 42,59 | 42,59 | class OracleMaskLoss(torch.nn.Module):
def __init__(self):
super().__init__()
self.gt_mask = None
self.loss = SigmoidBinaryCrossEntropyLoss(from_sigmoid=True)
self.predictor = None
self.history = []
def set_gt_mask(self, gt_mask):
self.gt_mask = gt_mask
s... | class OracleMaskLoss(torch.nn.Module):
def __init__(self):
super().__init__()
self.gt_mask = None
self.loss = SigmoidBinaryCrossEntropyLoss(from_sigmoid=True)
self.predictor = None
self.history = []
def set_gt_mask(self, gt_mask):
self.gt_mask = gt_mask
s... | gt_mask = self.gt_mask.to(result.device)
if self.predictor.object_roi is not None:
r1, r2, c1, c2 = self.predictor.object_roi[:4]
gt_mask = gt_mask[:, :, r1:r2 + 1, c1:c2 + 1]
gt_mask = torch.nn.functional.interpolate(gt_mask, result.size()[2:], mode='bilinear', alig... | gt_mask = self.gt_mask.to(result.device)
if self.predictor.object_roi is not None:
r1, r2, c1, c2 = self.predictor.object_roi[:4]
gt_mask = gt_mask[:, :, r1:r2 + 1, c1:c2 + 1]
gt_mask = torch.nn.functional.interpolate(gt_mask, result.size()[2:], mode='bilinear', alig... | 66 | 142 | 321 | 463 | import torch
from ...model.losses import SigmoidBinaryCrossEntropyLoss
class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
pos_diff = (1 - result) * pos_mask
pos_target = torch.sum... | import torch
from ...model.losses import SigmoidBinaryCrossEntropyLoss
class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
pos_diff = (1 - result) * pos_mask
pos_target = torch.sum... |
47,674 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/transforms/base.py | 8,9 | 4,24 | transform | BaseTransform | 9,11 | 9,11 | class BaseTransform(object):
def __init__(self):
self.image_changed = False
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY def inv_transform(self, prob_map):
raise NotImplementedError
def reset(self):
raise NotImplementedError
def get_state(self):
raise... | class BaseTransform(object):
def __init__(self):
self.image_changed = False
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY def inv_transform(self, prob_map):
raise NotImplementedError
def reset(self):
raise NotImplementedError
def get_state(self):
raise... | raise NotImplementedError
| raise NotImplementedError
| 2 | 128 | 11 | 139 | import torch
class BaseTransform(object):
def __init__(self):
self.image_changed = False
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY def inv_transform(self, prob_map):
raise NotImplementedError
def reset(self):
raise NotImplementedError
def get_state(self)... | import torch
class BaseTransform(object):
def __init__(self):
self.image_changed = False
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY def inv_transform(self, prob_map):
raise NotImplementedError
def reset(self):
raise NotImplementedError
def get_state(self)... |
47,675 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/transforms/base.py | 25,26 | 24,39 | transform | SigmoidForPred | 26,28 | 26,28 | class SigmoidForPred(BaseTransform):
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY def inv_transform(self, prob_map):
return torch.sigmoid(prob_map)
def reset(self):
pass
def get_state(self):
return None
def set_state(self, state):
pass
| class SigmoidForPred(BaseTransform):
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY def inv_transform(self, prob_map):
return torch.sigmoid(prob_map)
def reset(self):
pass
def get_state(self):
return None
def set_state(self, state):
pass
| return image_nd, clicks_lists
| return image_nd, clicks_lists
| 3 | 104 | 14 | 118 | import torch
class BaseTransform(object):
def __init__(self):
self.image_changed = False
def transform(self, image_nd, clicks_lists):
raise NotImplementedError
def inv_transform(self, prob_map):
raise NotImplementedError
def reset(self):
raise NotImplementedError
... | import torch
class BaseTransform(object):
def __init__(self):
self.image_changed = False
def transform(self, image_nd, clicks_lists):
raise NotImplementedError
def inv_transform(self, prob_map):
raise NotImplementedError
def reset(self):
raise NotImplementedError
... |
47,676 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/transforms/flip.py | 23,24 | 7,38 | inv_transform | AddHorizontalFlip | 24,30 | 24,30 | class AddHorizontalFlip(BaseTransform):
def transform(self, image_nd, clicks_lists):
assert len(image_nd.shape) == 4
image_nd = torch.cat([image_nd, torch.flip(image_nd, dims=[3])], dim=0)
image_width = image_nd.shape[3]
clicks_lists_flipped = []
for clicks_list in clicks_li... | class AddHorizontalFlip(BaseTransform):
def transform(self, image_nd, clicks_lists):
assert len(image_nd.shape) == 4
image_nd = torch.cat([image_nd, torch.flip(image_nd, dims=[3])], dim=0)
image_width = image_nd.shape[3]
clicks_lists_flipped = []
for clicks_list in clicks_li... | assert len(prob_map.shape) == 4 and prob_map.shape[0] % 2 == 0
num_maps = prob_map.shape[0] // 2
prob_map, prob_map_flipped = prob_map[:num_maps], prob_map[num_maps:]
return 0.5 * (prob_map + torch.flip(prob_map_flipped, dims=[3]))
| assert len(prob_map.shape) == 4 and prob_map.shape[0] % 2 == 0
num_maps = prob_map.shape[0] // 2
prob_map, prob_map_flipped = prob_map[:num_maps], prob_map[num_maps:]
return 0.5 * (prob_map + torch.flip(prob_map_flipped, dims=[3]))
| 27 | 290 | 113 | 403 | import torch
from ..clicker import Click
from .base import BaseTransform
class AddHorizontalFlip(BaseTransform):
def transform(self, image_nd, clicks_lists):
assert len(image_nd.shape) == 4
image_nd = torch.cat([image_nd, torch.flip(image_nd, dims=[3])], dim=0)
image_width = image_nd.sha... | import torch
from ..clicker import Click
from .base import BaseTransform
class AddHorizontalFlip(BaseTransform):
def transform(self, image_nd, clicks_lists):
assert len(image_nd.shape) == 4
image_nd = torch.cat([image_nd, torch.flip(image_nd, dims=[3])], dim=0)
image_width = image_nd.sha... |
47,677 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/transforms/limit_longest_side.py | 8,9 | 4,23 | transform | LimitLongestSide | 9,23 | 9,23 | class LimitLongestSide(ZoomIn):
def __init__(self, max_size=800):
super().__init__(target_size=max_size, skip_clicks=0)
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY | class LimitLongestSide(ZoomIn):
def __init__(self, max_size=800):
super().__init__(target_size=max_size, skip_clicks=0)
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY | assert image_nd.shape[0] == 1 and len(clicks_lists) == 1
image_max_size = max(image_nd.shape[2:4])
self.image_changed = False
if image_max_size <= self.target_size:
return image_nd, clicks_lists
self._input_image = image_nd
self._object_roi = (0, image_nd.sh... | assert image_nd.shape[0] == 1 and len(clicks_lists) == 1
image_max_size = max(image_nd.shape[2:4])
self.image_changed = False
if image_max_size <= self.target_size:
return image_nd, clicks_lists
self._input_image = image_nd
self._object_roi = (0, image_nd.sh... | 48 | 77 | 228 | 305 | from .zoom_in import ZoomIn, get_roi_image_nd
class LimitLongestSide(ZoomIn):
def __init__(self, max_size=800):
super().__init__(target_size=max_size, skip_clicks=0)
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY | from .zoom_in import ZoomIn, get_roi_image_nd
class LimitLongestSide(ZoomIn):
def __init__(self, max_size=800):
super().__init__(target_size=max_size, skip_clicks=0)
def transform(self, image_nd, clicks_lists):
FILL_FUNC_BODY |
47,679 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/inference/transforms/zoom_in.py | 65,66 | 8,127 | inv_transform | ZoomIn | 66,85 | 66,85 | class ZoomIn(BaseTransform):
def __init__(self,
target_size=400,
skip_clicks=1,
expansion_ratio=1.4,
min_crop_size=200,
recompute_thresh_iou=0.5,
prob_thresh=0.50):
super().__init__()
self.target_si... | class ZoomIn(BaseTransform):
def __init__(self,
target_size=400,
skip_clicks=1,
expansion_ratio=1.4,
min_crop_size=200,
recompute_thresh_iou=0.5,
prob_thresh=0.50):
super().__init__()
self.target_si... | if self._object_roi is None:
self._prev_probs = prob_map.cpu().numpy()
return prob_map
assert prob_map.shape[0] == 1
rmin, rmax, cmin, cmax = self._object_roi
prob_map = torch.nn.functional.interpolate(prob_map, size=(rmax - rmin + 1, cmax - cmin + 1),
... | if self._object_roi is None:
self._prev_probs = prob_map.cpu().numpy()
return prob_map
assert prob_map.shape[0] == 1
rmin, rmax, cmin, cmax = self._object_roi
prob_map = torch.nn.functional.interpolate(prob_map, size=(rmax - rmin + 1, cmax - cmin + 1),
... | 63 | 1,309 | 282 | 1,591 | import torch
from ..clicker import Click
from ...utils.misc import get_bbox_iou, get_bbox_from_mask, expand_bbox, clamp_bbox
from .base import BaseTransform
class ZoomIn(BaseTransform):
def __init__(self,
target_size=400,
skip_clicks=1,
expansion_ratio=1.4,
... | import torch
from ..clicker import Click
from ...utils.misc import get_bbox_iou, get_bbox_from_mask, expand_bbox, clamp_bbox
from .base import BaseTransform
class ZoomIn(BaseTransform):
def __init__(self,
target_size=400,
skip_clicks=1,
expansion_ratio=1.4,
... |
47,681 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/initializer.py | 66,68 | 47,79 | get_bilinear_kernel | Bilinear | 69,79 | 68,79 | class Bilinear(Initializer):
def __init__(self, scale, groups, in_channels, **kwargs):
super().__init__(**kwargs)
self.scale = scale
self.groups = groups
self.in_channels = in_channels
def _init_weight(self, data):
"""Reset the weight and bias."""
bilinear_kernel... | class Bilinear(Initializer):
def __init__(self, scale, groups, in_channels, **kwargs):
super().__init__(**kwargs)
self.scale = scale
self.groups = groups
self.in_channels = in_channels
def _init_weight(self, data):
"""Reset the weight and bias."""
bilinear_kernel... | kernel_size = 2 * scale - scale % 2
scale = (kernel_size + 1) // 2
center = scale - 0.5 * (1 + kernel_size % 2)
og = np.ogrid[:kernel_size, :kernel_size]
kernel = (1 - np.abs(og[0] - center) / scale) * (1 - np.abs(og[1] - center) / scale)
return torch.tensor(kernel, dty... | """Generate a bilinear upsampling kernel."""
kernel_size = 2 * scale - scale % 2
scale = (kernel_size + 1) // 2
center = scale - 0.5 * (1 + kernel_size % 2)
og = np.ogrid[:kernel_size, :kernel_size]
kernel = (1 - np.abs(og[0] - center) / scale) * (1 - np.abs(og[1] - cent... | 51 | 228 | 141 | 369 | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... |
47,682 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/initializer.py | 37,38 | 6,47 | _init_gamma | Initializer | 38,43 | 38,43 | class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d,
... | class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d,
... | if self.gamma is None:
nn.init.constant_(data, 1.0)
else:
nn.init.normal_(data, 1.0, self.gamma)
| if self.gamma is None:
nn.init.constant_(data, 1.0)
else:
nn.init.normal_(data, 1.0, self.gamma)
| 10 | 420 | 55 | 475 | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... |
47,683 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/initializer.py | 87,88 | 79,106 | _init_weight | XavierGluon | 88,106 | 88,106 | class XavierGluon(Initializer):
def __init__(self, rnd_type='uniform', factor_type='avg', magnitude=3, **kwargs):
super().__init__(**kwargs)
self.rnd_type = rnd_type
self.factor_type = factor_type
self.magnitude = float(magnitude)
def _init_weight(self, arr):
FILL_FUNC_BODY | class XavierGluon(Initializer):
def __init__(self, rnd_type='uniform', factor_type='avg', magnitude=3, **kwargs):
super().__init__(**kwargs)
self.rnd_type = rnd_type
self.factor_type = factor_type
self.magnitude = float(magnitude)
def _init_weight(self, arr):
FILL_FUNC_BODY | fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(arr)
if self.factor_type == 'avg':
factor = (fan_in + fan_out) / 2.0
elif self.factor_type == 'in':
factor = fan_in
elif self.factor_type == 'out':
factor = fan_out
else:
rais... | fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(arr)
if self.factor_type == 'avg':
factor = (fan_in + fan_out) / 2.0
elif self.factor_type == 'in':
factor = fan_in
elif self.factor_type == 'out':
factor = fan_out
else:
rais... | 58 | 114 | 223 | 337 | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... |
47,684 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/is_deeplab_model.py | 9,13 | 1,87 | get_deeplab_model | 13,30 | 13,30 | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
def get_deeplab_model(backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
norm_layer=nn.BatchNorm2d, backbone_norm_layer=None,
... | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
def get_deeplab_model(backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
norm_layer=nn.BatchNorm2d, backbone_norm_layer=None,
... | model = DistMapsModel(
feature_extractor=DeepLabV3Plus(backbone=backbone,
ch=deeplab_ch,
project_dropout=aspp_dropout,
norm_layer=norm_layer,
backbo... | model = DistMapsModel(
feature_extractor=DeepLabV3Plus(backbone=backbone,
ch=deeplab_ch,
project_dropout=aspp_dropout,
norm_layer=norm_layer,
backbo... | 22 | 885 | 210 | 1,095 | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
def get_deeplab_model(backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
norm_layer=nn.BatchNorm2d, backbone_norm_layer=None,
... | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
def get_deeplab_model(backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
norm_layer=nn.BatchNorm2d, backbone_norm_layer=None,
... | |
47,685 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/is_deeplab_model.py | 68,69 | 30,87 | load_weights | DistMapsModel | 69,74 | 69,74 | class DistMapsModel(nn.Module):
def __init__(self, feature_extractor, head, norm_layer=nn.BatchNorm2d, use_rgb_conv=True,
cpu_dist_maps=False, norm_radius=260):
super(DistMapsModel, self).__init__()
if use_rgb_conv:
self.rgb_conv = nn.Sequential(
nn.Conv... | class DistMapsModel(nn.Module):
def __init__(self, feature_extractor, head, norm_layer=nn.BatchNorm2d, use_rgb_conv=True,
cpu_dist_maps=False, norm_radius=260):
super(DistMapsModel, self).__init__()
if use_rgb_conv:
self.rgb_conv = nn.Sequential(
nn.Conv... | current_state_dict = self.state_dict()
new_state_dict = torch.load(path_to_weights, map_location='cpu')
current_state_dict.update(new_state_dict)
self.load_state_dict(current_state_dict)
| current_state_dict = self.state_dict()
new_state_dict = torch.load(path_to_weights, map_location='cpu')
current_state_dict.update(new_state_dict)
self.load_state_dict(current_state_dict)
| 9 | 661 | 79 | 740 | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
def get_deeplab_model(backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
norm_layer=nn.BatchNorm2d, backbone_norm_layer=None,
... | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
def get_deeplab_model(backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
norm_layer=nn.BatchNorm2d, backbone_norm_layer=None,
... |
47,686 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/is_hrnet_model.py | 8,11 | 1,88 | get_hrnet_model | 11,24 | 11,24 | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.hrnet_ocr import HighResolutionNet
def get_hrnet_model(width=48, ocr_width=256, small=False, norm_radius=260,
use_rgb_conv=True, with_aux_output=False, cpu_dist_maps=False,
norm_layer=nn.BatchNorm2d):
... | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.hrnet_ocr import HighResolutionNet
def get_hrnet_model(width=48, ocr_width=256, small=False, norm_radius=260,
use_rgb_conv=True, with_aux_output=False, cpu_dist_maps=False,
norm_layer=nn.BatchNorm2d):
... | model = DistMapsHRNetModel(
feature_extractor=HighResolutionNet(width=width, ocr_width=ocr_width, small=small,
num_classes=1, norm_layer=norm_layer),
use_rgb_conv=use_rgb_conv,
with_aux_output=with_aux_output,
norm_layer=norm_layer,
... | model = DistMapsHRNetModel(
feature_extractor=HighResolutionNet(width=width, ocr_width=ocr_width, small=small,
num_classes=1, norm_layer=norm_layer),
use_rgb_conv=use_rgb_conv,
with_aux_output=with_aux_output,
norm_layer=norm_layer,
... | 16 | 1,045 | 143 | 1,188 | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.hrnet_ocr import HighResolutionNet
def get_hrnet_model(width=48, ocr_width=256, small=False, norm_radius=260,
use_rgb_conv=True, with_aux_output=False, cpu_dist_maps=False,
norm_layer=nn.BatchNorm2d):
... | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.hrnet_ocr import HighResolutionNet
def get_hrnet_model(width=48, ocr_width=256, small=False, norm_radius=260,
use_rgb_conv=True, with_aux_output=False, cpu_dist_maps=False,
norm_layer=nn.BatchNorm2d):
... | |
47,687 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/is_hrnet_model.py | 43,44 | 24,88 | forward | DistMapsHRNetModel | 44,67 | 44,67 | class DistMapsHRNetModel(nn.Module):
def __init__(self, feature_extractor, use_rgb_conv=True, with_aux_output=False,
norm_layer=nn.BatchNorm2d, norm_radius=260, cpu_dist_maps=False):
super(DistMapsHRNetModel, self).__init__()
self.with_aux_output = with_aux_output
if use_rg... | class DistMapsHRNetModel(nn.Module):
def __init__(self, feature_extractor, use_rgb_conv=True, with_aux_output=False,
norm_layer=nn.BatchNorm2d, norm_radius=260, cpu_dist_maps=False):
super(DistMapsHRNetModel, self).__init__()
self.with_aux_output = with_aux_output
if use_rg... | coord_features = self.dist_maps(image, points)
if self.rgb_conv is not None:
x = self.rgb_conv(torch.cat((image, coord_features), dim=1))
else:
c1, c2 = torch.chunk(coord_features, 2, dim=1)
c3 = torch.ones_like(c1)
coord_features = torch.cat((c1,... | coord_features = self.dist_maps(image, points)
if self.rgb_conv is not None:
x = self.rgb_conv(torch.cat((image, coord_features), dim=1))
else:
c1, c2 = torch.chunk(coord_features, 2, dim=1)
c3 = torch.ones_like(c1)
coord_features = torch.cat((c1,... | 73 | 591 | 362 | 953 | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.hrnet_ocr import HighResolutionNet
def get_hrnet_model(width=48, ocr_width=256, small=False, norm_radius=260,
use_rgb_conv=True, with_aux_output=False, cpu_dist_maps=False,
norm_layer=nn.BatchNorm2d):
... | import torch
import torch.nn as nn
from .ops import DistMaps
from .modeling.hrnet_ocr import HighResolutionNet
def get_hrnet_model(width=48, ocr_width=256, small=False, norm_radius=260,
use_rgb_conv=True, with_aux_output=False, cpu_dist_maps=False,
norm_layer=nn.BatchNorm2d):
... |
47,688 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/losses.py | 88,89 | 70,113 | forward | FocalLoss | 89,113 | 89,113 | class FocalLoss(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, num_class=None,
eps=1e-9, size_average=True, scale=1.0):
super(FocalLoss, self).__init__()
self._axis = axis
self._alp... | class FocalLoss(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, num_class=None,
eps=1e-9, size_average=True, scale=1.0):
super(FocalLoss, self).__init__()
self._axis = axis
self._alp... | if not self._from_logits:
pred = F.sigmoid(pred)
one_hot = label > 0
pt = torch.where(one_hot, pred, 1 - pred)
t = label != -1
alpha = torch.where(one_hot, self._alpha * t, (1 - self._alpha) * t)
beta = (1 - pt) ** self._gamma
loss = -alpha * beta *... | if not self._from_logits:
pred = F.sigmoid(pred)
one_hot = label > 0
pt = torch.where(one_hot, pred, 1 - pred)
t = label != -1
alpha = torch.where(one_hot, self._alpha * t, (1 - self._alpha) * t)
beta = (1 - pt) ** self._gamma
loss = -alpha * beta *... | 92 | 230 | 351 | 581 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_de... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_de... |
47,689 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/losses.py | 30,31 | 9,70 | forward | NormalizedFocalLossSigmoid | 31,66 | 31,66 | class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_delimeter=True,
eps=1e-12, scale=1.0,
ignore_label=-1):
super(NormalizedFo... | class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_delimeter=True,
eps=1e-12, scale=1.0,
ignore_label=-1):
super(NormalizedFo... | one_hot = label > 0
sample_weight = label != self._ignore_label
if not self._from_logits:
pred = torch.sigmoid(pred)
alpha = torch.where(one_hot, self._alpha * sample_weight, (1 - self._alpha) * sample_weight)
pt = torch.where(one_hot, pred, 1 - pred)
pt = t... | one_hot = label > 0
sample_weight = label != self._ignore_label
if not self._from_logits:
pred = torch.sigmoid(pred)
alpha = torch.where(one_hot, self._alpha * sample_weight, (1 - self._alpha) * sample_weight)
pt = torch.where(one_hot, pred, 1 - pred)
pt = t... | 146 | 327 | 607 | 934 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_de... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_de... |
47,690 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/losses.py | 121,122 | 113,135 | forward | SigmoidBinaryCrossEntropyLoss | 122,135 | 122,135 | class SigmoidBinaryCrossEntropyLoss(nn.Module):
def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, ignore_label=-1):
super(SigmoidBinaryCrossEntropyLoss, self).__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
self._weight = weight if weigh... | class SigmoidBinaryCrossEntropyLoss(nn.Module):
def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, ignore_label=-1):
super(SigmoidBinaryCrossEntropyLoss, self).__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
self._weight = weight if weigh... | label = label.view(pred.size())
sample_weight = label != self._ignore_label
label = torch.where(sample_weight, label, torch.zeros_like(label))
if not self._from_sigmoid:
loss = torch.relu(pred) - pred * label + F.softplus(-torch.abs(pred))
else:
eps = 1e-... | label = label.view(pred.size())
sample_weight = label != self._ignore_label
label = torch.where(sample_weight, label, torch.zeros_like(label))
if not self._from_sigmoid:
loss = torch.relu(pred) - pred * label + F.softplus(-torch.abs(pred))
else:
eps = 1e-... | 57 | 145 | 212 | 357 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_de... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, size_average=True, detach_de... |
47,691 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/metrics.py | 86,87 | 1,102 | _compute_iou | 87,102 | 87,102 | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... | if ignore_mask is not None:
pred_mask = torch.where(ignore_mask, torch.zeros_like(pred_mask), pred_mask)
reduction_dims = misc.get_dims_with_exclusion(gt_mask.dim(), 0)
union = torch.mean((pred_mask | gt_mask).float(), dim=reduction_dims).detach().cpu().numpy()
intersection = torch.mean((pred_m... | if ignore_mask is not None:
pred_mask = torch.where(ignore_mask, torch.zeros_like(pred_mask), pred_mask)
reduction_dims = misc.get_dims_with_exclusion(gt_mask.dim(), 0)
union = torch.mean((pred_mask | gt_mask).float(), dim=reduction_dims).detach().cpu().numpy()
intersection = torch.mean((pred_m... | 51 | 1,053 | 232 | 1,285 | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... | |
47,692 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/metrics.py | 44,45 | 29,86 | update | AdaptiveIoU | 45,67 | 45,67 | class AdaptiveIoU(TrainMetric):
def __init__(self, init_thresh=0.4, thresh_step=0.025, thresh_beta=0.99, iou_beta=0.9,
ignore_label=-1, from_logits=True,
pred_output='instances', gt_output='instances'):
super().__init__(pred_outputs=(pred_output,), gt_outputs=(gt_output,))
... | class AdaptiveIoU(TrainMetric):
def __init__(self, init_thresh=0.4, thresh_step=0.025, thresh_beta=0.99, iou_beta=0.9,
ignore_label=-1, from_logits=True,
pred_output='instances', gt_output='instances'):
super().__init__(pred_outputs=(pred_output,), gt_outputs=(gt_output,))
... | gt_mask = gt > 0
if self._from_logits:
pred = torch.sigmoid(pred)
gt_mask_area = torch.sum(gt_mask, dim=(1, 2)).detach().cpu().numpy()
if np.all(gt_mask_area == 0):
return
ignore_mask = gt == self._ignore_label
max_iou = _compute_iou(pred > self.... | gt_mask = gt > 0
if self._from_logits:
pred = torch.sigmoid(pred)
gt_mask_area = torch.sum(gt_mask, dim=(1, 2)).detach().cpu().numpy()
if np.all(gt_mask_area == 0):
return
ignore_mask = gt == self._ignore_label
max_iou = _compute_iou(pred > self.... | 89 | 490 | 371 | 861 | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... |
47,693 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/metrics.py | 18,19 | 7,29 | reset_epoch_stats | TrainMetric | 19,21 | 19,21 | class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):
raise NotImplementedError
def reset_epoch_stat... | class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):
raise NotImplementedError
def reset_epoch_stat... | raise NotImplementedError
| raise NotImplementedError
| 2 | 166 | 11 | 177 | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... | import torch
import numpy as np
from ..utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):... |
47,694 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/basic_blocks.py | 23,24 | 6,27 | forward | ConvHead | 24,27 | 24,27 | class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(num_layers):
convhead.extend([
... | class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(num_layers):
convhead.extend([
... | return self.convhead(inputs[0])
| return self.convhead(inputs[0])
| 2 | 237 | 18 | 255 | import torch.nn as nn
from ...model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(n... | import torch.nn as nn
from ...model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(n... |
47,695 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/basic_blocks.py | 51,52 | 27,57 | forward | SepConvHead | 52,57 | 52,57 | class SepConvHead(nn.Module):
def __init__(self, num_outputs, in_channels, mid_channels, num_layers=1,
kernel_size=3, padding=1, dropout_ratio=0.0, dropout_indx=0,
norm_layer=nn.BatchNorm2d):
super(SepConvHead, self).__init__()
sepconvhead = []
for i in ra... | class SepConvHead(nn.Module):
def __init__(self, num_outputs, in_channels, mid_channels, num_layers=1,
kernel_size=3, padding=1, dropout_ratio=0.0, dropout_indx=0,
norm_layer=nn.BatchNorm2d):
super(SepConvHead, self).__init__()
sepconvhead = []
for i in ra... | x = inputs[0]
return self.layers(x)
| x = inputs[0]
return self.layers(x)
| 5 | 329 | 25 | 354 | import torch.nn as nn
from ...model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(n... | import torch.nn as nn
from ...model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(n... |
47,696 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/basic_blocks.py | 70,71 | 57,72 | forward | SeparableConv2d | 71,72 | 71,72 | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, dw_kernel, dw_padding, dw_stride=1,
activation=None, use_bias=False, norm_layer=None):
super(SeparableConv2d, self).__init__()
_activation = ops.select_activation_function(activation)
self.body =... | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, dw_kernel, dw_padding, dw_stride=1,
activation=None, use_bias=False, norm_layer=None):
super(SeparableConv2d, self).__init__()
_activation = ops.select_activation_function(activation)
self.body =... | return self.body(x)
| return self.body(x)
| 2 | 267 | 10 | 277 | import torch.nn as nn
from ...model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(n... | import torch.nn as nn
from ...model import ops
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init__()
convhead = []
for i in range(n... |
47,698 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/deeplab_v3.py | 51,52 | 12,84 | load_pretrained_weights | DeepLabV3Plus | 52,64 | 52,64 | class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
backbone_norm_layer=None,
ch=256,
project_dropout=0.5,
inference_mode=False,
**kwargs):
super(DeepLabV3Plus, self).__init__(... | class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
backbone_norm_layer=None,
ch=256,
project_dropout=0.5,
inference_mode=False,
**kwargs):
super(DeepLabV3Plus, self).__init__(... | pretrained = ResNetBackbone(backbone=self.backbone_name, pretrained_base=True,
norm_layer=self.backbone_norm_layer, **self._kwargs)
backbone_state_dict = self.backbone.state_dict()
pretrained_state_dict = pretrained.state_dict()
backbone_state_dict.up... | pretrained = ResNetBackbone(backbone=self.backbone_name, pretrained_base=True,
norm_layer=self.backbone_norm_layer, **self._kwargs)
backbone_state_dict = self.backbone.state_dict()
pretrained_state_dict = pretrained.state_dict()
backbone_state_dict.up... | 23 | 721 | 166 | 887 | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... |
47,699 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/deeplab_v3.py | 144,145 | 115,150 | forward | _ASPP | 145,150 | 145,150 | class _ASPP(nn.Module):
def __init__(self, in_channels, atrous_rates, out_channels=256,
project_dropout=0.5, norm_layer=nn.BatchNorm2d):
super(_ASPP, self).__init__()
b0 = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=F... | class _ASPP(nn.Module):
def __init__(self, in_channels, atrous_rates, out_channels=256,
project_dropout=0.5, norm_layer=nn.BatchNorm2d):
super(_ASPP, self).__init__()
b0 = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=F... | x = torch.cat([block(x) for block in self.concurent], dim=1)
return self.project(x)
| x = torch.cat([block(x) for block in self.concurent], dim=1)
return self.project(x)
| 10 | 445 | 43 | 488 | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... |
47,700 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/deeplab_v3.py | 162,163 | 150,167 | forward | _AsppPooling | 163,167 | 163,167 | class _AsppPooling(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer):
super(_AsppPooling, self).__init__()
self.gap = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_s... | class _AsppPooling(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer):
super(_AsppPooling, self).__init__()
self.gap = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_s... | pool = self.gap(x)
return F.interpolate(pool, x.size()[2:], mode='bilinear', align_corners=True)
| pool = self.gap(x)
return F.interpolate(pool, x.size()[2:], mode='bilinear', align_corners=True)
| 8 | 169 | 50 | 219 | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... |
47,701 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/deeplab_v3.py | 111,112 | 99,115 | forward | _DeepLabHead | 112,115 | 112,115 | class _DeepLabHead(nn.Module):
def __init__(self, out_channels, in_channels, mid_channels=256, norm_layer=nn.BatchNorm2d):
super(_DeepLabHead, self).__init__()
self.block = nn.Sequential(
SeparableConv2d(in_channels=in_channels, out_channels=mid_channels, dw_kernel=3,
... | class _DeepLabHead(nn.Module):
def __init__(self, out_channels, in_channels, mid_channels=256, norm_layer=nn.BatchNorm2d):
super(_DeepLabHead, self).__init__()
self.block = nn.Sequential(
SeparableConv2d(in_channels=in_channels, out_channels=mid_channels, dw_kernel=3,
... | return self.block(x)
| return self.block(x)
| 2 | 254 | 14 | 268 | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... |
47,702 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/deeplab_v3.py | 95,96 | 84,99 | forward | _SkipProject | 96,99 | 96,99 | class _SkipProject(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d):
super(_SkipProject, self).__init__()
_activation = ops.select_activation_function("relu")
self.skip_project = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=... | class _SkipProject(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d):
super(_SkipProject, self).__init__()
_activation = ops.select_activation_function("relu")
self.skip_project = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=... | return self.skip_project(x)
| return self.skip_project(x)
| 2 | 157 | 16 | 173 | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
from ...model import ops
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='resnet50', norm_layer=nn.BatchNorm2d,
... |
47,703 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/hrnet_ocr.py | 83,84 | 13,155 | _make_fuse_layers | HighResolutionModule | 84,125 | 84,125 | class HighResolutionModule(nn.Module):
def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
num_channels, fuse_method,multi_scale_output=True,
norm_layer=nn.BatchNorm2d, align_corners=True):
super(HighResolutionModule, self).__init__()
self._check_br... | class HighResolutionModule(nn.Module):
def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
num_channels, fuse_method,multi_scale_output=True,
norm_layer=nn.BatchNorm2d, align_corners=True):
super(HighResolutionModule, self).__init__()
self._check_br... | if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
... | if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
... | 91 | 1,313 | 566 | 1,879 | import os
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .ocr import SpatialOCR_Module, SpatialGather_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
relu_inplace = True
class HighResolutionModule(nn.Module):
def __init__(self, num_branch... | import os
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .ocr import SpatialOCR_Module, SpatialGather_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
relu_inplace = True
class HighResolutionModule(nn.Module):
def __init__(self, num_branch... |
47,705 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/ocr.py | 117,118 | 63,142 | forward | ObjectAttentionBlock2D | 118,142 | 118,142 | class ObjectAttentionBlock2D(nn.Module):
'''
The basic implementation for object context block
Input:
N X C X H X W
Parameters:
in_channels : the dimension of the input feature map
key_channels : the dimension after the key/query transform
scale : c... | class ObjectAttentionBlock2D(nn.Module):
'''
The basic implementation for object context block
Input:
N X C X H X W
Parameters:
in_channels : the dimension of the input feature map
key_channels : the dimension after the key/query transform
scale : c... | batch_size, h, w = x.size(0), x.size(2), x.size(3)
if self.scale > 1:
x = self.pool(x)
query = self.f_pixel(x).view(batch_size, self.key_channels, -1)
query = query.permute(0, 2, 1)
key = self.f_object(proxy).view(batch_size, self.key_channels, -1)
value = se... | batch_size, h, w = x.size(0), x.size(2), x.size(3)
if self.scale > 1:
x = self.pool(x)
query = self.f_pixel(x).view(batch_size, self.key_channels, -1)
query = query.permute(0, 2, 1)
key = self.f_object(proxy).view(batch_size, self.key_channels, -1)
value = se... | 89 | 878 | 397 | 1,275 | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... |
47,706 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/ocr.py | 19,20 | 7,30 | forward | SpatialGather_Module | 20,30 | 20,30 | class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
def __init__(self, cls_num=0, scale=1):
super(SpatialGather_Module, self).__in... | class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
def __init__(self, cls_num=0, scale=1):
super(SpatialGather_Module, self).__in... | batch_size, c, h, w = probs.size(0), probs.size(1), probs.size(2), probs.size(3)
probs = probs.view(batch_size, c, -1)
feats = feats.view(batch_size, feats.size(1), -1)
feats = feats.permute(0, 2, 1) # batch x hw x c
probs = F.softmax(self.scale * probs, dim=2) # batch x k x hw... | batch_size, c, h, w = probs.size(0), probs.size(1), probs.size(2), probs.size(3)
probs = probs.view(batch_size, c, -1)
feats = feats.view(batch_size, feats.size(1), -1)
feats = feats.permute(0, 2, 1) # batch x hw x c
probs = F.softmax(self.scale * probs, dim=2) # batch x k x hw... | 58 | 132 | 209 | 341 | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... |
47,707 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/ocr.py | 55,56 | 30,63 | forward | SpatialOCR_Module | 56,63 | 56,63 | class SpatialOCR_Module(nn.Module):
"""
Implementation of the OCR module:
We aggregate the global object representation to update the representation for each pixel.
"""
def __init__(self,
in_channels,
key_channels,
out_channels,
sc... | class SpatialOCR_Module(nn.Module):
"""
Implementation of the OCR module:
We aggregate the global object representation to update the representation for each pixel.
"""
def __init__(self,
in_channels,
key_channels,
out_channels,
sc... | context = self.object_context_block(feats, proxy_feats)
output = self.conv_bn_dropout(torch.cat([context, feats], 1))
return output
| context = self.object_context_block(feats, proxy_feats)
output = self.conv_bn_dropout(torch.cat([context, feats], 1))
return output
| 11 | 310 | 61 | 371 | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... |
47,708 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/resnet.py | 29,30 | 5,40 | forward | ResNetBackbone | 30,40 | 30,40 | class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
pretrained = resnet34_v1b(pretrained=pretrained_base, dilated=dilated, **kwargs)
elif b... | class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
pretrained = resnet34_v1b(pretrained=pretrained_base, dilated=dilated, **kwargs)
elif b... | x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
c1 = self.layer1(x)
c2 = self.layer2(c1)
c3 = self.layer3(c2)
c4 = self.layer4(c3)
return c1, c2, c3, c4
| x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
c1 = self.layer1(x)
c2 = self.layer2(c1)
c3 = self.layer3(c2)
c4 = self.layer4(c3)
return c1, c2, c3, c4
| 29 | 380 | 120 | 500 | import torch
from .resnetv1b import resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
... | import torch
from .resnetv1b import resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
... |
47,710 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/resnetv1b.py | 23,24 | 6,42 | forward | BasicBlockV1b | 24,42 | 24,42 | class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BasicBlockV1b, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride... | class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BasicBlockV1b, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
ret... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
ret... | 36 | 271 | 129 | 400 | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... |
47,711 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/resnetv1b.py | 62,63 | 42,85 | forward | BottleneckV1b | 63,85 | 63,85 | class BottleneckV1b(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BottleneckV1b, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
... | class BottleneckV1b(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(BottleneckV1b, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = se... | residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = se... | 45 | 311 | 167 | 478 | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... |
47,712 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/modeling/resnetv1b.py | 153,155 | 85,217 | _make_layer | ResNetV1b | 155,197 | 155,197 | class ResNetV1b(nn.Module):
""" Pre-trained ResNetV1b Model, which produces the strides of 8 featuremaps at conv5.
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
class... | class ResNetV1b(nn.Module):
""" Pre-trained ResNetV1b Model, which produces the strides of 8 featuremaps at conv5.
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
class... | downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = []
if avg_down:
if dilation == 1:
downsample.append(
nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_inclu... | downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = []
if avg_down:
if dilation == 1:
downsample.append(
nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_inclu... | 115 | 1,357 | 584 | 1,941 | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... |
47,713 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/ops.py | 9,10 | 1,84 | select_activation_function | 10,23 | 10,23 | import torch
from torch import nn as nn
import numpy as np
from . import initializer as initializer
from ..utils.cython import get_dist_maps
def select_activation_function(activation):
FILL_FUNC_BODYclass BilinearConvTranspose2d(nn.ConvTranspose2d):
def __init__(self, in_channels, out_channels, scale, groups=1):... | import torch
from torch import nn as nn
import numpy as np
from . import initializer as initializer
from ..utils.cython import get_dist_maps
def select_activation_function(activation):
FILL_FUNC_BODYclass BilinearConvTranspose2d(nn.ConvTranspose2d):
def __init__(self, in_channels, out_channels, scale, groups=1):... | if isinstance(activation, str):
if activation.lower() == 'relu':
return nn.ReLU
elif activation.lower() == 'softplus':
return nn.Softplus
else:
raise ValueError(f"Unknown activation type {activation}")
elif isinstance(activation, nn.Module):
re... | if isinstance(activation, str):
if activation.lower() == 'relu':
return nn.ReLU
elif activation.lower() == 'softplus':
return nn.Softplus
else:
raise ValueError(f"Unknown activation type {activation}")
elif isinstance(activation, nn.Module):
re... | 32 | 944 | 125 | 1,069 | import torch
from torch import nn as nn
import numpy as np
from . import initializer as initializer
from ..utils.cython import get_dist_maps
def select_activation_function(activation):
FILL_FUNC_BODYclass BilinearConvTranspose2d(nn.ConvTranspose2d):
def __init__(self, in_channels, out_channels, scale, groups=1):... | import torch
from torch import nn as nn
import numpy as np
from . import initializer as initializer
from ..utils.cython import get_dist_maps
def select_activation_function(activation):
FILL_FUNC_BODYclass BilinearConvTranspose2d(nn.ConvTranspose2d):
def __init__(self, in_channels, out_channels, scale, groups=1):... | |
47,714 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/ops.py | 46,47 | 39,84 | get_coord_features | DistMaps | 47,82 | 47,82 | class DistMaps(nn.Module):
def __init__(self, norm_radius, spatial_scale=1.0, cpu_mode=False):
super(DistMaps, self).__init__()
self.spatial_scale = spatial_scale
self.norm_radius = norm_radius
self.cpu_mode = cpu_mode
def get_coord_features(self, points, batchsize, rows, cols):... | class DistMaps(nn.Module):
def __init__(self, norm_radius, spatial_scale=1.0, cpu_mode=False):
super(DistMaps, self).__init__()
self.spatial_scale = spatial_scale
self.norm_radius = norm_radius
self.cpu_mode = cpu_mode
def get_coord_features(self, points, batchsize, rows, cols):... | if self.cpu_mode:
coords = []
for i in range(batchsize):
norm_delimeter = self.spatial_scale * self.norm_radius
coords.append(get_dist_maps(points[i].cpu().float().numpy(), rows, cols,
norm_delimeter))
... | if self.cpu_mode:
coords = []
for i in range(batchsize):
norm_delimeter = self.spatial_scale * self.norm_radius
coords.append(get_dist_maps(points[i].cpu().float().numpy(), rows, cols,
norm_delimeter))
... | 126 | 164 | 601 | 765 | import torch
from torch import nn as nn
import numpy as np
from . import initializer as initializer
from ..utils.cython import get_dist_maps
def select_activation_function(activation):
if isinstance(activation, str):
if activation.lower() == 'relu':
return nn.ReLU
elif activation.lowe... | import torch
from torch import nn as nn
import numpy as np
from . import initializer as initializer
from ..utils.cython import get_dist_maps
def select_activation_function(activation):
if isinstance(activation, str):
if activation.lower() == 'relu':
return nn.ReLU
elif activation.lowe... |
47,715 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/model/syncbn/modules/functional/_csrc.py | 27,28 | 1,55 | _load_C_extensions | 28,54 | 28,54 | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | this_dir = os.path.dirname(os.path.abspath(__file__))
this_dir = os.path.join(this_dir, "csrc")
main_file = glob.glob(os.path.join(this_dir, "*.cpp"))
sources_cpu = glob.glob(os.path.join(this_dir, "cpu", "*.cpp"))
sources_cuda = glob.glob(os.path.join(this_dir, "cuda", "*.cu"))
sources = main... | this_dir = os.path.dirname(os.path.abspath(__file__))
this_dir = os.path.join(this_dir, "csrc")
main_file = glob.glob(os.path.join(this_dir, "*.cpp"))
sources_cpu = glob.glob(os.path.join(this_dir, "cpu", "*.cpp"))
sources_cuda = glob.glob(os.path.join(this_dir, "cuda", "*.cu"))
sources = main... | 66 | 179 | 360 | 539 | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | |
47,716 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/utils/cython/_get_dist_maps.pyxbld | 3,4 | 1,8 | make_ext | 4,8 | 4,8 | import numpy
def make_ext(modname, pyxfilename):
FILL_FUNC_BODY | import numpy
def make_ext(modname, pyxfilename):
FILL_FUNC_BODY | from distutils.extension import Extension
return Extension(modname, [pyxfilename],
include_dirs=[numpy.get_include()],
extra_compile_args=['-O3'], language='c++')
| from distutils.extension import Extension
return Extension(modname, [pyxfilename],
include_dirs=[numpy.get_include()],
extra_compile_args=['-O3'], language='c++')
| 10 | 28 | 62 | 90 | import numpy
def make_ext(modname, pyxfilename):
FILL_FUNC_BODY | import numpy
def make_ext(modname, pyxfilename):
FILL_FUNC_BODY | |
47,717 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs/utils/misc.py | 15,16 | 1,63 | get_unique_labels | 16,19 | 16,19 | from functools import partial
import torch
import numpy as np
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def get_unique_labels(mask):
FILL_FUNC_BODYdef get_bbox_from_mask(mask):
rows = np.any(mask, axis=1... | from functools import partial
import torch
import numpy as np
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def get_unique_labels(mask):
FILL_FUNC_BODYdef get_bbox_from_mask(mask):
rows = np.any(mask, axis=1... | return np.nonzero(np.bincount(mask.flatten() + 1))[0] - 1
| return np.nonzero(np.bincount(mask.flatten() + 1))[0] - 1
| 6 | 686 | 33 | 719 | from functools import partial
import torch
import numpy as np
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def get_unique_labels(mask):
FILL_FUNC_BODYdef get_bbox_from_mask(mask):
rows = np.any(mask, axis=1... | from functools import partial
import torch
import numpy as np
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def get_unique_labels(mask):
FILL_FUNC_BODYdef get_bbox_from_mask(mask):
rows = np.any(mask, axis=1... | |
47,718 | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem | /home/hieuvd/dung_repo/raw/stars_03/hkchengrex_XMem/XMem/inference/interact/fbrs_controller.py | 48,49 | 6,54 | undo | FBRSController | 49,54 | 49,54 | class FBRSController:
def __init__(self, checkpoint_path, device='cuda:0', max_size=800):
model = utils.load_is_model(checkpoint_path, device, cpu_dist_maps=True, norm_radius=260)
# Predictor params
zoomin_params = {
'skip_clicks': 1,
'target_size': 480,
... | class FBRSController:
def __init__(self, checkpoint_path, device='cuda:0', max_size=800):
model = utils.load_is_model(checkpoint_path, device, cpu_dist_maps=True, norm_radius=260)
# Predictor params
zoomin_params = {
'skip_clicks': 1,
'target_size': 480,
... | self.controller.undo_click()
if len(self.controller.probs_history) == 0:
return None
else:
return (self.controller.probs_history[-1][1]>0.5).float() | self.controller.undo_click()
if len(self.controller.probs_history) == 0:
return None
else:
return (self.controller.probs_history[-1][1]>0.5).float() | 10 | 484 | 63 | 547 | import torch
from .fbrs.controller import InteractiveController
from .fbrs.inference import utils
class FBRSController:
def __init__(self, checkpoint_path, device='cuda:0', max_size=800):
model = utils.load_is_model(checkpoint_path, device, cpu_dist_maps=True, norm_radius=260)
# Predictor params
... | import torch
from .fbrs.controller import InteractiveController
from .fbrs.inference import utils
class FBRSController:
def __init__(self, checkpoint_path, device='cuda:0', max_size=800):
model = utils.load_is_model(checkpoint_path, device, cpu_dist_maps=True, norm_radius=260)
# Predictor params
... |
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