Add test for onnx

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

test_onnx.py

@@ -1,110 +1,97 @@
 from towhee import ops
 from timm_image import TimmImage
+import torch
+import numpy
 import onnx
+import onnxruntime
 
-f = open('onnx.csv', 'a+')
-f.write('model_name, run_op, save_onnx, check_onnx\n')
+import os
+from pathlib import Path
+import logging
+import platform
+import psutil
 
 # models = TimmImage.supported_model_names()[:1]
-models = [
-    'vgg11',
-    'resnet18',
-    'resnetv2_50',
-    'seresnet33ts',
-    'skresnet18',
-    'resnext26ts',
-    'seresnext26d_32x4d',
-    'skresnext50_32x4d',
-    'convit_base',
-    'inception_v4',
-    'efficientnet_b0',
-    'tf_efficientnet_b0',
-    'swin_base_patch4_window7_224',
-    'vit_base_patch8_224',
-    'beit_base_patch16_224',
-    'convnext_base',
-    'crossvit_9_240',
-    'convmixer_768_32',
-    'coat_lite_mini',
-    'inception_v3',
-    'cait_m36_384',
-    'cspdarknet53',
-    'deit_base_distilled_patch16_224',
-    'densenet121',
-    'dla34',
-    'dm_nfnet_f0',
-    'nf_regnet_b1',
-    'nf_resnet50',
-    'dpn68',
-    'ese_vovnet19b_dw',
-    'fbnetc_100',
-    'fbnetv3_b',
-    'halonet26t',
-    'eca_halonext26ts',
-    'sehalonet33ts',
-    'hardcorenas_a',
-    'hrnet_w18',
-    'jx_nest_base',
-    'lcnet_050',
-    'levit_128',
-    'mixer_b16_224',
-    'mixnet_s',
-    'mnasnet_100',
-    'mobilenetv2_050',
-    'mobilenetv3_large_100',
-    'nasnetalarge',
-    'pit_b_224',
-    'pnasnet5large',
-    'regnetx_002',
-    'repvgg_a2',
-    'res2net50_14w_8s',
-    'res2next50',
-    'resmlp_12_224',
-    'resnest14d',
-    'rexnet_100',
-    'selecsls42b',
-    'semnasnet_075',
-    'tinynet_a',
-    'tnt_s_patch16_224',
-    'tresnet_l',
-    'twins_pcpvt_base',
-    'visformer_small',
-    'xception',
-    'xcit_large_24_p8_224',
-    'ghostnet_100',
-    'gmlp_s16_224',
-    'lambda_resnet26rpt_256',
-    'spnasnet_100',
-]
+models = ['resnet50']
 
-decoder = ops.image_decode()
-data = decoder('./towhee.jpeg')
+atol = 1e-3
+log_path = 'timm_onnx.log'
+f = open('onnx.csv', 'w+')
+f.write('model,load_op,save_onnx,check_onnx,run_onnx,accuracy\n')
 
+logger = logging.getLogger('timm_onnx')
+logger.setLevel(logging.DEBUG)
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+fh = logging.FileHandler(log_path)
+fh.setLevel(logging.DEBUG)
+fh.setFormatter(formatter)
+logger.addHandler(fh)
+ch = logging.StreamHandler()
+ch.setLevel(logging.ERROR)
+ch.setFormatter(formatter)
+logger.addHandler(ch)
+
+logger.debug(f'machine: {platform.platform()}-{platform.processor()}')
+logger.debug(f'free/available/total mem: {round(psutil.virtual_memory().free / (1024.0 ** 3))}'
+             f'/{round(psutil.virtual_memory().available / (1024.0 ** 3))}'
+             f'/{round(psutil.virtual_memory().total / (1024.0 ** 3))} GB')
+logger.debug(f'cpu: {psutil.cpu_count()}')
+
+
+status = None
 for name in models:
-    f.write(f'{name},')
+    logger.info(f'***{name}***')
+    saved_name = name.replace('/', '-')
+    onnx_path = f'saved/onnx/{saved_name}.onnx'
+    if status:
+        f.write(','.join(status) + '\n')
+    status = [name] + ['fail'] * 5
+
+    op = TimmImage(model_name=name, device='cpu')
+    data = torch.rand((1,) + op.config['input_size'])
     try:
-        op = TimmImage(model_name=name)
-        out1 = op(data)
-        f.write('success,')
+        out1 = op.model(data).detach().numpy()
+        logger.info('OP LOADED.')
+        status[1] = 'success'
     except Exception as e:
-        f.write('fail\n')
-        print(f'Fail to load op for {name}: {e}')
+        logger.error(f'FAIL TO LOAD OP: {e}')
         continue
     try:
         op.save_model(format='onnx')
-        f.write('success,')
+        logger.info('ONNX SAVED.')
+        status[2] = 'success'
     except Exception as e:
-        f.write('fail\n')
-        print(f'Fail to save onnx for {name}: {e}')
+        logger.error(f'FAIL TO SAVE ONNX: {e}')
         continue
     try:
-        saved_name = name.replace('/', '-')
-        onnx_model = onnx.load(f'saved/onnx/{saved_name}.onnx')
+        try:
+            onnx_model = onnx.load(onnx_path)
             onnx.checker.check_model(onnx_model)
-        f.write('success')
+        except Exception:
+            saved_onnx = onnx.load(onnx_path, load_external_data=False)
+            onnx.checker.check_model(saved_onnx)
+        logger.info('ONNX CHECKED.')
+        status[3] = 'success'
     except Exception as e:
-        f.write('fail\n')
-        print(f'Fail to check onnx for {name}: {e}')
+        logger.error(f'FAIL TO CHECK ONNX: {e}')
         continue
-    f.write('\n')
+    try:
+        sess = onnxruntime.InferenceSession(onnx_path,
+                                            providers=onnxruntime.get_available_providers())
+
+        out2 = sess.run(None, input_feed={'input_0': data.detach().numpy()})
+        logger.info('ONNX WORKED.')
+        status[4] = 'success'
+        if numpy.allclose(out1, out2, atol=atol):
+            logger.info('Check accuracy: OK')
+            status[5] = 'success'
+        else:
+            logger.info(f'Check accuracy: atol is larger than {atol}.')
+    except Exception as e:
+        logger.error(f'FAIL TO RUN ONNX: {e}')
+        continue
+
+if status:
+    f.write(','.join(status) + '\n')
+
 print('Finished.')

timm_image.py

@@ -37,7 +37,7 @@ from timm.models.factory import create_model
 import warnings
 
 warnings.filterwarnings('ignore')
-log = logging.getLogger()
+log = logging.getLogger('timm_op')
 
 
 @register(output_schema=['vec'])
@@ -53,13 +53,22 @@ class TimmImage(NNOperator):
             Whether skip image transforms.
     """
 
-    def __init__(self, model_name: str, num_classes: int = 1000, skip_preprocess: bool = False) -> None:
+    def __init__(self,
+                 model_name: str = 'resnet50',
+                 num_classes: int = 1000,
+                 skip_preprocess: bool = False,
+                 pretrained: bool = True,
+                 device: str = None
+                 ) -> None:
         super().__init__()
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = device
         self.model_name = model_name
-        self.model = create_model(self.model_name, pretrained=True, num_classes=num_classes)
-        self.model.to(self.device)
+        self.model = create_model(self.model_name, pretrained=pretrained, num_classes=num_classes)
         self.model.eval()
+        self.model.to(self.device)
+
         self.config = resolve_data_config({}, model=self.model)
         self.tfms = create_transform(**self.config)
         self.skip_tfms = skip_preprocess
@@ -120,9 +129,15 @@ class TimmImage(NNOperator):
                 torch.onnx.export(self.model,
                                   dummy_input,
                                   path,
-                                  input_names=["input"],
-                                  output_names=["output"],
-                                  opset_version=12)
+                                  input_names=['input_0'],
+                                  output_names=['output_0'],
+                                  opset_version=13,
+                                  dynamic_axes={
+                                      'input_0': {0: 'batch_size'},
+                                      'output_0': {0: 'batch_size'}
+                                  },
+                                  do_constant_folding=True
+                                  )
             except Exception as e:
                 log.error(f'Fail to save as onnx: {e}.')
                 raise RuntimeError(f'Fail to save as onnx: {e}.')
@@ -140,6 +155,10 @@ class TimmImage(NNOperator):
             to_remove = []
             assert set(to_remove).issubset(set(full_list))
             model_list = list(set(full_list) - set(to_remove))
+        elif format == 'onnx':
+            to_remove = []
+            assert set(to_remove).issubset(set(full_list))
+            model_list = list(set(full_list) - set(to_remove))
         # todo: elif format == 'torchscript':
         # todo: elif format == 'tensorrt'
         else: