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vision classification QAT tutorial: fix for DDP (redo) #2230

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May 18, 2020
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vision classification QAT tutorial: fix for DDP (redo) #2230

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5 changes: 3 additions & 2 deletions references/classification/train_quantization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,6 @@ def main(args):
print("Creating model", args.model)
# when training quantized models, we always start from a pre-trained fp32 reference model
model = torchvision.models.quantization.__dict__[args.model](pretrained=True, quantize=args.test_only)
model.to(device)

if not (args.test_only or args.post_training_quantize):
model.fuse_model()
Expand All @@ -66,6 +65,8 @@ def main(args):
step_size=args.lr_step_size,
gamma=args.lr_gamma)

model.to(device)

criterion = nn.CrossEntropyLoss()
model_without_ddp = model
if args.distributed:
Expand Down Expand Up @@ -129,7 +130,7 @@ def main(args):
print('Evaluate QAT model')

evaluate(model, criterion, data_loader_test, device=device)
quantized_eval_model = copy.deepcopy(model)
quantized_eval_model = copy.deepcopy(model_without_ddp)
quantized_eval_model.eval()
quantized_eval_model.to(torch.device('cpu'))
torch.quantization.convert(quantized_eval_model, inplace=True)
Expand Down