Create stylegan_model.py

stylegan_model.py

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+import math
+import pickle
+
+import torch
+from torch import distributed as dist
+from torch.utils.data.sampler import Sampler
+
+
+def get_rank():
+    if not dist.is_available():
+        return 0
+
+    if not dist.is_initialized():
+        return 0
+
+    return dist.get_rank()
+
+
+def synchronize():
+    if not dist.is_available():
+        return
+
+    if not dist.is_initialized():
+        return
+
+    world_size = dist.get_world_size()
+
+    if world_size == 1:
+        return
+
+    dist.barrier()
+
+
+def get_world_size():
+    if not dist.is_available():
+        return 1
+
+    if not dist.is_initialized():
+        return 1
+
+    return dist.get_world_size()
+
+
+def reduce_sum(tensor):
+    if not dist.is_available():
+        return tensor
+
+    if not dist.is_initialized():
+        return tensor
+
+    tensor = tensor.clone()
+    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
+
+    return tensor
+
+
+def gather_grad(params):
+    world_size = get_world_size()
+    
+    if world_size == 1:
+        return
+
+    for param in params:
+        if param.grad is not None:
+            dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
+            param.grad.data.div_(world_size)
+
+
+def all_gather(data):
+    world_size = get_world_size()
+
+    if world_size == 1:
+        return [data]
+
+    buffer = pickle.dumps(data)
+    storage = torch.ByteStorage.from_buffer(buffer)
+    tensor = torch.ByteTensor(storage).to('cuda')
+
+    local_size = torch.IntTensor([tensor.numel()]).to('cuda')
+    size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
+
+    if local_size != max_size:
+        padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
+        tensor = torch.cat((tensor, padding), 0)
+
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+
+    for size, tensor in zip(size_list, tensor_list):
+        buffer = tensor.cpu().numpy().tobytes()[:size]
+        data_list.append(pickle.loads(buffer))
+
+    return data_list
+
+
+def reduce_loss_dict(loss_dict):
+    world_size = get_world_size()
+
+    if world_size < 2:
+        return loss_dict
+
+    with torch.no_grad():
+        keys = []
+        losses = []
+
+        for k in sorted(loss_dict.keys()):
+            keys.append(k)
+            losses.append(loss_dict[k])
+
+        losses = torch.stack(losses, 0)
+        dist.reduce(losses, dst=0)
+
+        if dist.get_rank() == 0:
+            losses /= world_size
+
+        reduced_losses = {k: v for k, v in zip(keys, losses)}
+
+    return reduced_losses