classifier, export, torch seed updates

models/export.py

 
 import argparse
 import sys
+import time
 
 sys.path.append('./')  # to run '$ python *.py' files in subdirectories
 
 import models
 from models.experimental import attempt_load
 from utils.activations import Hardswish
-from utils.general import set_logging
+from utils.general import set_logging, check_img_size
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
     print(opt)
     set_logging()
+    t = time.time()
 
     # Input
     img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection
 
     # Load PyTorch model
     model = attempt_load(opt.weights, map_location=torch.device('cpu'))  # load FP32 model
+    labels = model.names
+
+    # Checks
+    gs = int(max(model.stride))  # grid size (max stride)
+    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
 
     # Update model
     for k, m in model.named_modules():
-        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
+        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
         if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
             m.act = Hardswish()  # assign activation
         # if isinstance(m, models.yolo.Detect):
 
         print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
         # convert model from torchscript and apply pixel scaling as per detect.py
-        model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
+        model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
         f = opt.weights.replace('.pt', '.mlmodel')  # filename
         model.save(f)
         print('CoreML export success, saved as %s' % f)
         print('CoreML export failure: %s' % e)
 
     # Finish
-    print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')
+    print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))

utils/general.py

 from tqdm import tqdm
 
 from utils.google_utils import gsutil_getsize
-from utils.torch_utils import init_seeds as init_torch_seeds
-from utils.torch_utils import is_parallel
+from utils.torch_utils import is_parallel, init_torch_seeds
 
 # Set printoptions
 torch.set_printoptions(linewidth=320, precision=5, profile='long')
 def init_seeds(seed=0):
     random.seed(seed)
     np.random.seed(seed)
-    init_torch_seeds(seed=seed)
+    init_torch_seeds(seed)
 
 
 def get_latest_run(search_dir='./runs'):

utils/torch_utils.py

 import torch.backends.cudnn as cudnn
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision.models as models
 
 logger = logging.getLogger(__name__)
 
 
-def init_seeds(seed=0):
+def init_torch_seeds(seed=0):
     torch.manual_seed(seed)
 
     # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
 
 def load_classifier(name='resnet101', n=2):
     # Loads a pretrained model reshaped to n-class output
-    model = models.__dict__[name](pretrained=True)
-
-    # Display model properties
-    input_size = [3, 224, 224]
-    input_space = 'RGB'
-    input_range = [0, 1]
-    mean = [0.485, 0.456, 0.406]
-    std = [0.229, 0.224, 0.225]
-    for x in ['input_size', 'input_space', 'input_range', 'mean', 'std']:
-        print(x + ' =', eval(x))
+    import torchvision
+    model = torchvision.models.__dict__[name](pretrained=True)
+
+    # ResNet model properties
+    # input_size = [3, 224, 224]
+    # input_space = 'RGB'
+    # input_range = [0, 1]
+    # mean = [0.485, 0.456, 0.406]
+    # std = [0.229, 0.224, 0.225]
 
     # Reshape output to n classes
     filters = model.fc.weight.shape[1]