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https://github.com/wassname/curl.git
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437 lines
13 KiB
Python
437 lines
13 KiB
Python
import torch
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import numpy as np
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import torch.nn as nn
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import gym
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import os
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from collections import deque
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import random
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from torch.utils.data import Dataset, DataLoader
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import time
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from skimage.util.shape import view_as_windows
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class eval_mode(object):
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def __init__(self, *models):
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self.models = models
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def __enter__(self):
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self.prev_states = []
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for model in self.models:
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self.prev_states.append(model.training)
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model.train(False)
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def __exit__(self, *args):
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for model, state in zip(self.models, self.prev_states):
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model.train(state)
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return False
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def soft_update_params(net, target_net, tau):
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for param, target_param in zip(net.parameters(), target_net.parameters()):
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target_param.data.copy_(
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tau * param.data + (1 - tau) * target_param.data
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)
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def set_seed_everywhere(seed):
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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def module_hash(module):
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result = 0
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for tensor in module.state_dict().values():
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result += tensor.sum().item()
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return result
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def make_dir(dir_path):
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try:
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os.mkdir(dir_path)
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except OSError:
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pass
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return dir_path
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def preprocess_obs(obs, bits=5):
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"""Preprocessing image, see https://arxiv.org/abs/1807.03039."""
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bins = 2**bits
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assert obs.dtype == torch.float32
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if bits < 8:
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obs = torch.floor(obs / 2**(8 - bits))
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obs = obs / bins
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obs = obs + torch.rand_like(obs) / bins
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obs = obs - 0.5
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return obs
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class ReplayBuffer(Dataset):
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"""Buffer to store environment transitions."""
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def __init__(self, obs_shape, action_shape, capacity, batch_size, device,image_size=84,transform=None):
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self.capacity = capacity
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self.batch_size = batch_size
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self.device = device
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self.image_size = image_size
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self.transform = transform
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# the proprioceptive obs is stored as float32, pixels obs as uint8
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obs_dtype = np.float32 if len(obs_shape) == 1 else np.uint8
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self.obses = np.empty((capacity, *obs_shape), dtype=obs_dtype)
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self.next_obses = np.empty((capacity, *obs_shape), dtype=obs_dtype)
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self.actions = np.empty((capacity, *action_shape), dtype=np.float32)
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self.rewards = np.empty((capacity, 1), dtype=np.float32)
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self.not_dones = np.empty((capacity, 1), dtype=np.float32)
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self.idx = 0
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self.last_save = 0
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self.full = False
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def add(self, obs, action, reward, next_obs, done):
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np.copyto(self.obses[self.idx], obs)
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np.copyto(self.actions[self.idx], action)
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np.copyto(self.rewards[self.idx], reward)
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np.copyto(self.next_obses[self.idx], next_obs)
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np.copyto(self.not_dones[self.idx], not done)
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self.idx = (self.idx + 1) % self.capacity
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self.full = self.full or self.idx == 0
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def sample_proprio(self):
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idxs = np.random.randint(
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0, self.capacity if self.full else self.idx, size=self.batch_size
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)
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obses = self.obses[idxs]
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next_obses = self.next_obses[idxs]
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obses = torch.as_tensor(obses, device=self.device).float()
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actions = torch.as_tensor(self.actions[idxs], device=self.device)
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rewards = torch.as_tensor(self.rewards[idxs], device=self.device)
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next_obses = torch.as_tensor(
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next_obses, device=self.device
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).float()
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not_dones = torch.as_tensor(self.not_dones[idxs], device=self.device)
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return obses, actions, rewards, next_obses, not_dones
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def sample_cpc(self):
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start = time.time()
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idxs = np.random.randint(
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0, self.capacity if self.full else self.idx, size=self.batch_size
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)
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obses = self.obses[idxs]
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next_obses = self.next_obses[idxs]
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pos = obses.copy()
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obses = fast_random_crop(obses, self.image_size)
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next_obses = fast_random_crop(next_obses, self.image_size)
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pos = fast_random_crop(pos, self.image_size)
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obses = torch.as_tensor(obses, device=self.device).float()
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next_obses = torch.as_tensor(
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next_obses, device=self.device
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).float()
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actions = torch.as_tensor(self.actions[idxs], device=self.device)
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rewards = torch.as_tensor(self.rewards[idxs], device=self.device)
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not_dones = torch.as_tensor(self.not_dones[idxs], device=self.device)
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pos = torch.as_tensor(pos, device=self.device).float()
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cpc_kwargs = dict(obs_anchor=obses, obs_pos=pos,
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time_anchor=None, time_pos=None)
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return obses, actions, rewards, next_obses, not_dones, cpc_kwargs
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def save(self, save_dir):
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if self.idx == self.last_save:
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return
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path = os.path.join(save_dir, '%d_%d.pt' % (self.last_save, self.idx))
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payload = [
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self.obses[self.last_save:self.idx],
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self.next_obses[self.last_save:self.idx],
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self.actions[self.last_save:self.idx],
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self.rewards[self.last_save:self.idx],
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self.not_dones[self.last_save:self.idx]
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]
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self.last_save = self.idx
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torch.save(payload, path)
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def load(self, save_dir):
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chunks = os.listdir(save_dir)
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chucks = sorted(chunks, key=lambda x: int(x.split('_')[0]))
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for chunk in chucks:
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start, end = [int(x) for x in chunk.split('.')[0].split('_')]
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path = os.path.join(save_dir, chunk)
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payload = torch.load(path)
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assert self.idx == start
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self.obses[start:end] = payload[0]
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self.next_obses[start:end] = payload[1]
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self.actions[start:end] = payload[2]
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self.rewards[start:end] = payload[3]
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self.not_dones[start:end] = payload[4]
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self.idx = end
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def __getitem__(self, idx):
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idx = np.random.randint(
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0, self.capacity if self.full else self.idx, size=1
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)
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idx = idx[0]
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obs = self.obses[idx]
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action = self.actions[idx]
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reward = self.rewards[idx]
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next_obs = self.next_obses[idx]
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not_done = self.not_dones[idx]
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if self.transform:
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obs = self.transform(obs)
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next_obs = self.transform(next_obs)
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return obs, action, reward, next_obs, not_done
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def __len__(self):
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return self.capacity
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class FrameStack(gym.Wrapper):
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def __init__(self, env, k):
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gym.Wrapper.__init__(self, env)
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self._k = k
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self._frames = deque([], maxlen=k)
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shp = env.observation_space.shape
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self.observation_space = gym.spaces.Box(
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low=0,
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high=1,
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shape=((shp[0] * k,) + shp[1:]),
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dtype=env.observation_space.dtype
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)
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self._max_episode_steps = env._max_episode_steps
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def reset(self):
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obs = self.env.reset()
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for _ in range(self._k):
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self._frames.append(obs)
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return self._get_obs()
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def step(self, action):
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obs, reward, done, info = self.env.step(action)
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self._frames.append(obs)
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return self._get_obs(), reward, done, info
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def _get_obs(self):
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assert len(self._frames) == self._k
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return np.concatenate(list(self._frames), axis=0)
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"""
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Various transforms
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"""
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class RandomCrop(object):
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"""Crop randomly the image in a sample.
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Args:
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output_size (tuple or int): Desired output size. If int, square crop
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is made.
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"""
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def __init__(self, output_size):
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assert isinstance(output_size, (int, tuple))
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if isinstance(output_size, int):
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self.output_size = (output_size, output_size)
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else:
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assert len(output_size) == 2
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self.output_size = output_size
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def __call__(self, image):
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h, w = image.shape[1:]
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new_h, new_w = self.output_size
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top = np.random.randint(0, h - new_h)
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left = np.random.randint(0, w - new_w)
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image = image[:, top: top + new_h, left: left + new_w]
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return image
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def random_crop(imgs,output_size):
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h, w = imgs.shape[2:]
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new_h, new_w = output_size, output_size
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if h > new_h:
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top = np.random.randint(0, h - new_h)
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left = np.random.randint(0, w - new_w)
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imgs = imgs[:,:, top: top + new_h, left: left + new_w]
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return imgs
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def fast_random_crop(imgs, output_size):
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"""
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Vectorized way to do random crop using sliding windows
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and picking out random ones
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args:
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imgs, batch images with shape (B,C,H,W)
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"""
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# batch size
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n = imgs.shape[0]
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img_size = imgs.shape[-1]
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crop_max = img_size - output_size
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imgs = np.transpose(imgs, (0, 2, 3, 1))
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w1 = np.random.randint(0, crop_max, n)
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h1 = np.random.randint(0, crop_max, n)
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# creates all sliding windows combinations of size (output_size)
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windows = view_as_windows(
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imgs, (1, output_size, output_size, 1))[..., 0,:,:, 0]
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# selects a random window for each batch element
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cropped_imgs = windows[np.arange(n), w1, h1]
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return cropped_imgs
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def random_flip(imgs, prob=0.2):
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B = imgs.shape[0]
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N = int(prob*B)
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flipped_imgs = imgs[..., ::-1].copy()
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idxs = np.random.choice(B, size=(N,), replace=False)
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imgs[idxs] = flipped_imgs[idxs]
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return imgs
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def time_flip(imgs,device):
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time_flipped_imgs = imgs[:,::-1, ...].copy()
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all_imgs = np.concatenate((imgs, time_flipped_imgs), 0)
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return all_imgs
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def grayscale(imgs,device):
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# imgs: b x c x h x w
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b, c, h, w = imgs.shape
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frames = c // 3
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imgs = imgs.view([b,frames,3,h,w])
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imgs = imgs[:, :, 0, ...] * 0.2989 + imgs[:, :, 1, ...] * 0.587 + imgs[:, :, 2, ...] * 0.114
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imgs = imgs.type(torch.uint8).float()
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# assert len(imgs.shape) == 3, imgs.shape
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imgs = imgs[:, :, None, :, :]
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imgs = imgs * torch.ones([1, 1, 3, 1, 1], dtype=imgs.dtype).float().to(device) # broadcast tiling
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return imgs
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def random_grayscale(images,device,p=1.):
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# images: [B, C, H, W]
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gray_images = grayscale(images,device)
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rnd = np.random.uniform(0., 1., size=(images.shape[0],))
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mask = rnd <= p
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mask = torch.from_numpy(mask)
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frames = images.shape[1] // 3
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images = images.view(*gray_images.shape)
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mask = mask[:, None] * torch.ones([1, frames]).type(mask.dtype)
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mask = mask.type(images.dtype).to(device)
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mask = mask[:, :, None, None, None]
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return mask * gray_images + (1 - mask) * images
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def random_grayscale_stack(stack,device,p=0.5):
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# stack: B X C x H x W, C = num_frames * 3.
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bs, channels, h, w = stack.shape
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num_frames = channels // 3
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#stack = stack.view([-1, 3, h, w])
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stack = random_grayscale(stack, device,p=p)
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stack = stack.view([bs, -1, h, w])
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return stack
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def random_rotate(imgs):
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k = np.random.randint(4)
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imgs = np.ascontiguousarray(np.rot90(imgs,k=k,axes=(-2,-1)))
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return imgs
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def center_crop_image(image, output_size):
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h, w = image.shape[1:]
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new_h, new_w = output_size, output_size
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top = (h - new_h)//2
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left = (w - new_w)//2
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image = image[:, top:top + new_h, left:left + new_w]
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return image
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class CenterCrop(object):
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"""Center crop the image in a sample.
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Args:
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output_size (tuple or int): Desired output size. If int, square crop
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is made.
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"""
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def __init__(self, output_size):
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assert isinstance(output_size, (int,))
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self.output_size = (output_size, output_size)
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def __call__(self, image):
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h, w = image.shape[1:]
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new_h, new_w = self.output_size
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top = (h - new_h)//2
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left = (w - new_w)//2
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image = image[:, top: top + new_h, left: left + new_w]
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return image
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class ToTensor(object):
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"""Convert ndarrays in sample to Tensors."""
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def __call__(self, image,device):
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# torch image: C X H X W
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return torch.from_numpy(image,)
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class Grayscale(object):
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"""Convert ndarrays in sample to grayscale randomly."""
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def __init__(self, prob):
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self.prob = prob
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def __call__(self, image):
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if self.prob > np.random.uniform():
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image = self.rgb2gray(image)
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return image
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def rgb2gray(self, rgb):
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rgb = np.transpose(rgb, (1, 2, 0))
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rgb = np.expand_dims(np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140]), 0)
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rgb = np.repeat(rgb, 3, 0)
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return rgb.astype(np.uint8)
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class Flip(object):
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"""Convert ndarrays in sample to flip randomly."""
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def __init__(self, prob):
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self.prob = prob
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def __call__(self, image):
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if self.prob > np.random.uniform():
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image = self.flip(image)
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return image
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def flip(self, img):
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return np.transpose(img, (0, 2, 1))
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