mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
015088507f
- feature: use different default steps and image sizes depending on sampler and model selceted - fix: #110 use proper version in image metadata - refactor: samplers all have their own class that inherits from ImageSampler
137 lines
3.9 KiB
Python
137 lines
3.9 KiB
Python
import logging
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import os
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import sys
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from functools import partialmethod
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from shutil import rmtree
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import pytest
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import responses
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from tqdm import tqdm
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from urllib3 import HTTPConnectionPool
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from imaginairy import api
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from imaginairy.log_utils import suppress_annoying_logs_and_warnings
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from imaginairy.samplers import SAMPLER_TYPE_OPTIONS
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from imaginairy.utils import (
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fix_torch_group_norm,
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fix_torch_nn_layer_norm,
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get_device,
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platform_appropriate_autocast,
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)
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from tests import TESTS_FOLDER
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if "pytest" in str(sys.argv):
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suppress_annoying_logs_and_warnings()
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logger = logging.getLogger(__name__)
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SAMPLERS_FOR_TESTING = SAMPLER_TYPE_OPTIONS
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if get_device() == "mps:0":
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SAMPLERS_FOR_TESTING = ["plms", "k_euler_a"]
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elif get_device() == "cpu":
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SAMPLERS_FOR_TESTING = []
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@pytest.fixture(scope="session", autouse=True)
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def pre_setup():
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api.IMAGINAIRY_SAFETY_MODE = "disabled"
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suppress_annoying_logs_and_warnings()
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test_output_folder = f"{TESTS_FOLDER}/test_output"
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# delete the testoutput folder and recreate it
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try:
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rmtree(test_output_folder)
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except FileNotFoundError:
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pass
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os.makedirs(test_output_folder, exist_ok=True)
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orig_urlopen = HTTPConnectionPool.urlopen
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def urlopen_tattle(self, method, url, *args, **kwargs):
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# traceback.print_stack()
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# current_test = os.environ.get("PYTEST_CURRENT_TEST", "")
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# print(f"{current_test} {method} {self.host}{url}")
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result = orig_urlopen(self, method, url, *args, **kwargs)
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# raise HTTPError("NO NETWORK CALLS")
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return result
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HTTPConnectionPool.urlopen = urlopen_tattle
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tqdm.__init__ = partialmethod(tqdm.__init__, disable=True)
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# real_randn = torch.randn
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# def randn_tattle(*args, **kwargs):
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# print("RANDN CALL RANDN CALL")
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# traceback.print_stack()
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# return real_randn(*args, **kwargs)
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#
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# torch.randn = randn_tattle
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with fix_torch_nn_layer_norm(), fix_torch_group_norm(), platform_appropriate_autocast():
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yield
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@pytest.fixture(autouse=True)
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def reset_get_device():
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get_device.cache_clear()
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@pytest.fixture()
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def filename_base_for_outputs(request):
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filename_base = f"{TESTS_FOLDER}/test_output/{request.node.name}_"
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return filename_base
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@pytest.fixture()
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def filename_base_for_orig_outputs(request):
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filename_base = f"{TESTS_FOLDER}/test_output/{request.node.originalname}_"
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return filename_base
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@pytest.fixture(params=SAMPLERS_FOR_TESTING)
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def sampler_type(request):
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return request.param
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@pytest.fixture
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def mocked_responses():
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with responses.RequestsMock() as rsps:
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yield rsps
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def pytest_addoption(parser):
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parser.addoption(
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"--subset",
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action="store",
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default=None,
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help="Runs an exclusive subset of tests: '1/3', '2/3', '3/3'. Useful for distributed testing",
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)
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@pytest.hookimpl()
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def pytest_collection_modifyitems(config, items):
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"""Only select a subset of tests to run, based on the --subset option."""
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filtered_node_ids = set()
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node_ids = [f.nodeid for f in items]
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node_ids.sort()
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subset = config.getoption("--subset")
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if subset:
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partition_no, total_partitions = subset.split("/")
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partition_no, total_partitions = int(partition_no), int(total_partitions)
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if partition_no < 1 or partition_no > total_partitions:
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raise ValueError("Invalid subset")
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for i, node_id in enumerate(node_ids):
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if i % total_partitions == partition_no - 1:
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filtered_node_ids.add(node_id)
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items[:] = [i for i in items if i.nodeid in filtered_node_ids]
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print(
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f"Running subset {partition_no}/{total_partitions} {len(filtered_node_ids)} tests:"
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)
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filtered_node_ids = list(filtered_node_ids)
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filtered_node_ids.sort()
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for n in filtered_node_ids:
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print(f" {n}")
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