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import torch |
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from torch._utils import _flatten_dense_tensors |
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import numpy as np |
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class EMA: |
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def __init__(self, params, mu=0.999): |
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self.mu = mu |
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self.state = [(p, self.get_model_state(p)) for p in params if p.requires_grad] |
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def get_model_state(self, p): |
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return p.data.float().detach().clone() |
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def step(self): |
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for p, state in self.state: |
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state.mul_(self.mu).add_(1 - self.mu, p.data.float()) |
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def swap(self): |
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for p, state in self.state: |
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other_state = self.get_model_state(p) |
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p.data.copy_(state.type_as(p.data)) |
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state.copy_(other_state) |
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class CPUEMA: |
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def __init__(self, params, mu=0.999, freq=1): |
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self.mu = mu**freq |
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self.state = [(p, self.get_model_state(p)) for p in params if p.requires_grad] |
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self.freq = freq |
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self.steps = 0 |
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def get_model_state(self, p): |
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with torch.no_grad(): |
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state = p.data.float().detach().cpu().numpy() |
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return state |
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def step(self): |
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with torch.no_grad(): |
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self.steps += 1 |
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if self.steps % self.freq == 0: |
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for i in range(len(self.state)): |
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p, state = self.state[i] |
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state = torch.from_numpy(state).cuda() |
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state.mul_(self.mu).add_(1 - self.mu, p.data.float()) |
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self.state[i] = (p, state.cpu().numpy()) |
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def swap(self): |
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with torch.no_grad(): |
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for p, state in self.state: |
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other_state = self.get_model_state(p) |
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p.data.copy_(torch.from_numpy(state).type_as(p.data)) |
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np.copyto(state, other_state) |
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class FusedEMA: |
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def __init__(self, params, mu=0.999): |
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self.mu = mu |
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params = list(params) |
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self.params = {} |
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self.params['fp16'] = [p for p in params if p.requires_grad and p.data.dtype == torch.float16] |
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self.params['fp32'] = [p for p in params if p.requires_grad and p.data.dtype != torch.float16] |
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self.groups = [group for group in self.params.keys() if len(self.params[group]) > 0] |
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self.state = {} |
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for group in self.groups: |
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self.state[group] = self.get_model_state(group) |
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def get_model_state(self, group): |
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params = self.params[group] |
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return _flatten_dense_tensors([p.data.float() for p in params]) |
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def step(self): |
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for group in self.groups: |
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self.state[group].mul_(self.mu).add_(1 - self.mu, self.get_model_state(group)) |
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def swap(self): |
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for group in self.groups: |
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other_state = self.get_model_state(group) |
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state = self.state[group] |
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params = self.params[group] |
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offset = 0 |
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for p in params: |
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numel = p.data.numel() |
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p.data = state.narrow(0, offset, numel).view_as(p.data).type_as(p.data) |
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offset += numel |
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self.state[group] = other_state |
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