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pytorch-image-models/tests/test_models.py

438 lines
18 KiB

import pytest
import torch
import platform
import os
import fnmatch
try:
from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names, NodePathTracer
has_fx_feature_extraction = True
except ImportError:
has_fx_feature_extraction = False
import timm
from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
get_model_default_value
from timm.models.fx_features import _leaf_modules, _autowrap_functions
if hasattr(torch._C, '_jit_set_profiling_executor'):
# legacy executor is too slow to compile large models for unit tests
# no need for the fusion performance here
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(False)
# transformer models don't support many of the spatial / feature based model functionalities
NON_STD_FILTERS = [
'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit_*']
NUM_NON_STD = len(NON_STD_FILTERS)
# exclude models that cause specific test failures
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*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*']
else:
EXCLUDE_FILTERS = []
TARGET_FWD_SIZE = MAX_FWD_SIZE = 384
TARGET_BWD_SIZE = 128
MAX_BWD_SIZE = 320
MAX_FWD_OUT_SIZE = 448
TARGET_JIT_SIZE = 128
MAX_JIT_SIZE = 320
TARGET_FFEAT_SIZE = 96
MAX_FFEAT_SIZE = 256
TARGET_FWD_FX_SIZE = 128
MAX_FWD_FX_SIZE = 224
TARGET_BWD_FX_SIZE = 128
MAX_BWD_FX_SIZE = 224
def _get_input_size(model=None, model_name='', target=None):
if model is None:
assert model_name, "One of model or model_name must be provided"
input_size = get_model_default_value(model_name, 'input_size')
fixed_input_size = get_model_default_value(model_name, 'fixed_input_size')
min_input_size = get_model_default_value(model_name, 'min_input_size')
else:
default_cfg = model.default_cfg
input_size = default_cfg['input_size']
fixed_input_size = default_cfg.get('fixed_input_size', None)
min_input_size = default_cfg.get('min_input_size', None)
assert input_size is not None
if fixed_input_size:
return input_size
if min_input_size:
if target and max(input_size) > target:
input_size = min_input_size
else:
if target and max(input_size) > target:
input_size = tuple([min(x, target) for x in input_size])
return input_size
@pytest.mark.timeout(120)
@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"""
model = create_model(model_name, pretrained=False)
model.eval()
input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
if max(input_size) > MAX_FWD_SIZE:
pytest.skip("Fixed input size model > limit.")
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
if max(input_size) > MAX_BWD_SIZE:
pytest.skip("Fixed input size model > limit.")
model = create_model(model_name, pretrained=False, num_classes=42)
num_params = sum([x.numel() for x in model.parameters()])
model.train()
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
state_dict = model.state_dict()
cfg = model.default_cfg
pool_size = cfg['pool_size']
input_size = model.default_cfg['input_size']
if all([x <= MAX_FWD_OUT_SIZE for x in input_size]) and \
not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
# output sizes only checked if default res <= 448 * 448 to keep resource down
input_size = tuple([min(x, MAX_FWD_OUT_SIZE) for x in input_size])
input_tensor = torch.randn((batch_size, *input_size))
# test forward_features (always unpooled)
outputs = model.forward_features(input_tensor)
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 2
assert outputs.shape[-1] == model.num_features
# test model forward without pooling and classifier
model.reset_classifier(0, '') # reset classifier and set global pooling to pass-through
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
# FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
if 'pruned' not in model_name: # FIXME better pruned model handling
# test classifier + global pool deletion via __init__
model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
# FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
# check classifier name matches default_cfg
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs_non_std(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
state_dict = model.state_dict()
cfg = model.default_cfg
input_size = _get_input_size(model=model)
if max(input_size) > 320: # FIXME const
pytest.skip("Fixed input size model > limit.")
input_tensor = torch.randn((batch_size, *input_size))
outputs = model.forward_features(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
assert outputs.shape[1] == model.num_features
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
outputs = model.forward(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
assert len(outputs.shape) == 2
assert outputs.shape[1] == model.num_features
model = create_model(model_name, pretrained=False, num_classes=0).eval()
outputs = model.forward(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
assert len(outputs.shape) == 2
assert outputs.shape[1] == model.num_features
# check classifier name matches default_cfg
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
if 'GITHUB_ACTIONS' not in os.environ:
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(pretrained=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_load_pretrained(model_name, batch_size):
"""Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_features_pretrained(model_name, batch_size):
"""Create that pretrained weights load when features_only==True."""
create_model(model_name, pretrained=True, features_only=True)
EXCLUDE_JIT_FILTERS = [
'*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable
'dla*', 'hrnet*', 'ghostnet*', # hopefully fix at some point
'vit_large_*', 'vit_huge_*',
]
@pytest.mark.timeout(120)
@pytest.mark.parametrize(
'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_torchscript(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(model)
outputs = model(torch.randn((batch_size, *input_size)))
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
EXCLUDE_FEAT_FILTERS = [
'*pruned*', # hopefully fix at some point
] + NON_STD_FILTERS
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_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_features(model_name, batch_size):
"""Run a single forward pass with each model in feature extraction mode"""
model = create_model(model_name, pretrained=False, features_only=True)
model.eval()
expected_channels = model.feature_info.channels()
assert len(expected_channels) >= 4 # all models here should have at least 4 feature levels by default, some 5 or 6
input_size = _get_input_size(model=model, target=TARGET_FFEAT_SIZE)
if max(input_size) > MAX_FFEAT_SIZE:
pytest.skip("Fixed input size model > limit.")
outputs = model(torch.randn((batch_size, *input_size)))
assert len(expected_channels) == len(outputs)
for e, o in zip(expected_channels, outputs):
assert e == o.shape[1]
assert o.shape[0] == batch_size
assert not torch.isnan(o).any()
def _create_fx_model(model, train=False):
# This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
# So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
# node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
train_nodes, eval_nodes = get_graph_node_names(
model, tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
eval_return_nodes = [eval_nodes[-1]]
train_return_nodes = [train_nodes[-1]]
if train:
tracer = NodePathTracer(leaf_modules=list(_leaf_modules), autowrap_functions=list(_autowrap_functions))
graph = tracer.trace(model)
graph_nodes = list(reversed(graph.nodes))
output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
graph_node_names = [n.name for n in graph_nodes]
output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
train_return_nodes = [train_nodes[ix] for ix in output_node_indices]
fx_model = create_feature_extractor(
model, train_return_nodes=train_return_nodes, eval_return_nodes=eval_return_nodes,
tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
return fx_model
EXCLUDE_FX_FILTERS = []
# not enough memory to run fx on more models than other tests
if 'GITHUB_ACTIONS' in os.environ:
EXCLUDE_FX_FILTERS += ['beit_large*', 'swin_large*']
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx(model_name, batch_size):
"""
Symbolically trace each model and run single forward pass through the resulting GraphModule
Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
model = create_model(model_name, pretrained=False)
model.eval()
input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
if max(input_size) > MAX_FWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.")
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
model = _create_fx_model(model)
fx_outputs = tuple(model(inputs).values())
if isinstance(fx_outputs, tuple):
fx_outputs = torch.cat(fx_outputs)
assert torch.all(fx_outputs == outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward_fx(model_name, batch_size):
"""Symbolically trace each model and run single backward pass through the resulting GraphModule"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
if max(input_size) > MAX_BWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.")
model = create_model(model_name, pretrained=False, num_classes=42)
num_params = sum([x.numel() for x in model.parameters()])
model.train()
model = _create_fx_model(model, train=True)
outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
# reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
EXCLUDE_FX_JIT_FILTERS = [
'deit_*_distilled_patch16_224',
'levit*',
'pit_*_distilled_224',
] + EXCLUDE_FX_FILTERS
@pytest.mark.timeout(120)
@pytest.mark.parametrize(
'model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx_torchscript(model_name, batch_size):
"""Symbolically trace each model, script it, and run single forward pass"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(_create_fx_model(model))
outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'