rename /checkpoints to /weights
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0ae90d0fb7
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332fe002b3
10
models.py
10
models.py
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@ -266,8 +266,12 @@ class Darknet(nn.Module):
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return sum(output) if is_training else torch.cat(output, 1)
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def load_weights(self, weights_path):
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"""Parses and loads the weights stored in 'weights_path'"""
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def load_weights(self, weights_path, cutoff=-1):
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# Parses and loads the weights stored in 'weights_path'
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# @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
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if weights_path.endswith('darknet53.conv.74'):
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cutoff = 75
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# Open the weights file
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fp = open(weights_path, 'rb')
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@ -281,7 +285,7 @@ def load_weights(self, weights_path):
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fp.close()
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ptr = 0
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for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
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for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
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if module_def['type'] == 'convolutional':
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conv_layer = module[0]
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if module_def['batch_normalize']:
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2
train.py
2
train.py
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@ -73,6 +73,8 @@ def main(opt):
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del checkpoint # current, saved
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else:
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load_weights(model, 'weights/darknet53.conv.74') # load darknet53 weights (optional)
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if torch.cuda.device_count() > 1:
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print('Using ', torch.cuda.device_count(), ' GPUs')
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model = nn.DataParallel(model)
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@ -67,7 +67,7 @@ class load_images_and_labels(): # for training
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self.img_files = file.readlines()
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if platform == 'darwin': # MacOS (local)
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self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/DATA/coco/images')
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self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/data/coco/images')
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for path in self.img_files]
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else: # linux (gcp cloud)
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self.img_files = [path.replace('\n', '').replace('/images', '../coco/images') for path in self.img_files]
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@ -1,4 +1,5 @@
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#!/bin/bash
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wget https://pjreddie.com/media/files/darknet53.conv.74
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wget https://pjreddie.com/media/files/yolov3.weights
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wget https://storage.googleapis.com/ultralytics/yolov3.pt
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