mirror of
https://github.com/wassname/curl.git
synced 2026-08-21 11:14:16 +08:00
264 lines
8.4 KiB
Python
264 lines
8.4 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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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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from diy_gym.utils import flatten, unflatten
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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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os.makedirs(dir_path, exist_ok=True)
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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_space, action_space, capacity, batch_size, device, image_size=84, transform=None):
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obs_shape = flatten(obs_space.sample()).shape
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action_shape = action_space.shape
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self.obs_space = obs_space
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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, mixeds obs as uint8
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obs_dtype = np.float16 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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obs = flatten(obs)
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next_obs = flatten(next_obs)
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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 unflatten_obs(self, obs):
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obs = [unflatten(o, self.obs_space) for o in obs]
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obs = {
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k: np.stack([o[k] for o in obs])
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for k in obs[0].keys()
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}
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return obs
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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.as_tensor_obs(self.unflatten_obs(self.obses[idxs]))
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next_obses = self.as_tensor_obs(self.unflatten_obs(self.next_obses[idxs]))
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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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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_raw = self.unflatten_obs(self.obses[idxs])
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next_obses_raw = self.unflatten_obs(self.next_obses[idxs])
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# Split mixed obs into image and state
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state, obses = split_obs(obses_raw)
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next_state, next_obses = split_obs(next_obses_raw)
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pos = obses.copy()
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# Crop
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obses = random_crop(obses, self.image_size)
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next_obses = random_crop(next_obses, self.image_size)
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pos = random_crop(pos, self.image_size)
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# Recombine
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obses = self.as_tensor_obs(combine_obs(state, obses))
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next_obses = self.as_tensor_obs(combine_obs(next_state, next_obses))
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pos = self.as_tensor_obs(combine_obs(state, pos))
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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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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 as_tensor_obs(self, obses):
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obses['img'] = torch.as_tensor(obses['img'], device=self.device).float()
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obses['state'] = torch.as_tensor(obses['state'], device=self.device).float()
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return obses
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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.unflatten_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.unflatten_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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def 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 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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def split_obs(obs):
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"""Split a dict obs into state and images."""
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return obs['state'], obs['img']
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def combine_obs(state, img):
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return dict(state=state, img=img)
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def split_obs_shape(obs_shape):
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obs = np.zeros(obs_shape)
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state, img = split_obs(obs)
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return state.shape, img.shape
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