|
|
|
@ -1,15 +1,24 @@
|
|
|
|
|
import pytest
|
|
|
|
|
import torch
|
|
|
|
|
import platform
|
|
|
|
|
import os
|
|
|
|
|
import fnmatch
|
|
|
|
|
|
|
|
|
|
from timm import list_models, create_model
|
|
|
|
|
|
|
|
|
|
MAX_FWD_SIZE = 320
|
|
|
|
|
|
|
|
|
|
if 'GITHUB_ACTIONS' in os.environ and 'Linux' in platform.system():
|
|
|
|
|
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
|
|
|
|
|
EXCLUDE_FILTERS = ['*efficientnet_l2*']
|
|
|
|
|
else:
|
|
|
|
|
EXCLUDE_FILTERS = []
|
|
|
|
|
MAX_FWD_SIZE = 384
|
|
|
|
|
MAX_BWD_SIZE = 128
|
|
|
|
|
MAX_FWD_FEAT_SIZE = 448
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@pytest.mark.timeout(120)
|
|
|
|
|
@pytest.mark.parametrize('model_name', list_models())
|
|
|
|
|
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
|
|
|
|
|
@pytest.mark.parametrize('batch_size', [1])
|
|
|
|
|
def test_model_forward(model_name, batch_size):
|
|
|
|
|
"""Run a single forward pass with each model"""
|
|
|
|
@ -28,7 +37,8 @@ def test_model_forward(model_name, batch_size):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@pytest.mark.timeout(120)
|
|
|
|
|
@pytest.mark.parametrize('model_name', list_models(exclude_filters='dla*')) # DLA models have an issue TBD
|
|
|
|
|
# DLA models have an issue TBD, add them to exclusions
|
|
|
|
|
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + ['dla*']))
|
|
|
|
|
@pytest.mark.parametrize('batch_size', [2])
|
|
|
|
|
def test_model_backward(model_name, batch_size):
|
|
|
|
|
"""Run a single forward pass with each model"""
|
|
|
|
@ -65,7 +75,8 @@ def test_model_default_cfgs(model_name, batch_size):
|
|
|
|
|
pool_size = cfg['pool_size']
|
|
|
|
|
input_size = model.default_cfg['input_size']
|
|
|
|
|
|
|
|
|
|
if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and 'efficientnet_l2' not in model_name:
|
|
|
|
|
if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
|
|
|
|
|
not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
|
|
|
|
|
# pool size only checked if default res <= 448 * 448 to keep resource down
|
|
|
|
|
input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
|
|
|
|
|
outputs = model.forward_features(torch.randn((batch_size, *input_size)))
|
|
|
|
|