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TiKick

TiKick-main/setup.py

import os from setuptools import setup, find_packages import setuptools def get_version() -> str: # https://packaging.python.org/guides/single-sourcing-package-version/ init = open(os.path.join("tmarl", "__init__.py"), "r").read().split() return init[init.index("__version__") + 2][1:-1] setup( name="tmarl", # Replace with your own username version=get_version(), description="marl algorithms", long_description=open("README.md", encoding="utf8").read(), long_description_content_type="text/markdown", author="tmarl", author_email="tmarl_contact@tartrl.cn", packages=setuptools.find_packages(), classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", "Programming Language :: Python :: 3", "License :: OSI Approved :: Apache License", "Operating System :: OS Independent", ], keywords="multi-agent reinforcement learning algorithms pytorch", python_requires='>=3.6', )

py

TiKick

TiKick-main/tmarl/networks/policy_network.py

import torch import torch.nn as nn from tmarl.networks.utils.util import init, check from tmarl.networks.utils.mlp import MLPBase, MLPLayer from tmarl.networks.utils.rnn import RNNLayer from tmarl.networks.utils.act import ACTLayer from tmarl.networks.utils.popart import PopArt from tmarl.utils.util import get_shape_from_obs_space # networks are defined here class PolicyNetwork(nn.Module): def __init__(self, args, obs_space, action_space, device=torch.device("cpu")): super(PolicyNetwork, self).__init__() self.hidden_size = args.hidden_size self._gain = args.gain self._use_orthogonal = args.use_orthogonal self._activation_id = args.activation_id self._use_policy_active_masks = args.use_policy_active_masks self._use_naive_recurrent_policy = args.use_naive_recurrent_policy self._use_recurrent_policy = args.use_recurrent_policy self._use_influence_policy = args.use_influence_policy self._influence_layer_N = args.influence_layer_N self._use_policy_vhead = args.use_policy_vhead self._recurrent_N = args.recurrent_N self.tpdv = dict(dtype=torch.float32, device=device) obs_shape = get_shape_from_obs_space(obs_space) self._mixed_obs = False self.base = MLPBase(args, obs_shape, use_attn_internal=False, use_cat_self=True) input_size = self.base.output_size if self._use_naive_recurrent_policy or self._use_recurrent_policy: self.rnn = RNNLayer(input_size, self.hidden_size, self._recurrent_N, self._use_orthogonal) input_size = self.hidden_size if self._use_influence_policy: self.mlp = MLPLayer(obs_shape[0], self.hidden_size, self._influence_layer_N, self._use_orthogonal, self._activation_id) input_size += self.hidden_size self.act = ACTLayer(action_space, input_size, self._use_orthogonal, self._gain) if self._use_policy_vhead: init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][self._use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0)) if self._use_popart: self.v_out = init_(PopArt(input_size, 1, device=device)) else: self.v_out = init_(nn.Linear(input_size, 1)) self.to(device) def forward(self, obs, rnn_states, masks, available_actions=None, deterministic=False): if self._mixed_obs: for key in obs.keys(): obs[key] = check(obs[key]).to(**self.tpdv) else: obs = check(obs).to(**self.tpdv) rnn_states = check(rnn_states).to(**self.tpdv) masks = check(masks).to(**self.tpdv) if available_actions is not None: available_actions = check(available_actions).to(**self.tpdv) actor_features = self.base(obs) if self._use_naive_recurrent_policy or self._use_recurrent_policy: actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks) if self._use_influence_policy: mlp_obs = self.mlp(obs) actor_features = torch.cat([actor_features, mlp_obs], dim=1) actions, action_log_probs = self.act(actor_features, available_actions, deterministic) return actions, action_log_probs, rnn_states def evaluate_actions(self, obs, rnn_states, action, masks, available_actions=None, active_masks=None): if self._mixed_obs: for key in obs.keys(): obs[key] = check(obs[key]).to(**self.tpdv) else: obs = check(obs).to(**self.tpdv) rnn_states = check(rnn_states).to(**self.tpdv) action = check(action).to(**self.tpdv) masks = check(masks).to(**self.tpdv) if available_actions is not None: available_actions = check(available_actions).to(**self.tpdv) if active_masks is not None: active_masks = check(active_masks).to(**self.tpdv) actor_features = self.base(obs) if self._use_naive_recurrent_policy or self._use_recurrent_policy: actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks) if self._use_influence_policy: mlp_obs = self.mlp(obs) actor_features = torch.cat([actor_features, mlp_obs], dim=1) action_log_probs, dist_entropy = self.act.evaluate_actions(actor_features, action, available_actions, active_masks = active_masks if self._use_policy_active_masks else None) values = self.v_out(actor_features) if self._use_policy_vhead else None return action_log_probs, dist_entropy, values def get_policy_values(self, obs, rnn_states, masks): if self._mixed_obs: for key in obs.keys(): obs[key] = check(obs[key]).to(**self.tpdv) else: obs = check(obs).to(**self.tpdv) rnn_states = check(rnn_states).to(**self.tpdv) masks = check(masks).to(**self.tpdv) actor_features = self.base(obs) if self._use_naive_recurrent_policy or self._use_recurrent_policy: actor_features, rnn_states = self.rnn(actor_features, rnn_states, masks) if self._use_influence_policy: mlp_obs = self.mlp(obs) actor_features = torch.cat([actor_features, mlp_obs], dim=1) values = self.v_out(actor_features) return values

py

TiKick

TiKick-main/tmarl/networks/utils/distributions.py

import torch import torch.nn as nn from .util import init """ Modify standard PyTorch distributions so they are compatible with this code. """ # Standardize distribution interfaces # Categorical class FixedCategorical(torch.distributions.Categorical): def sample(self): return super().sample().unsqueeze(-1) def log_probs(self, actions): return ( super() .log_prob(actions.squeeze(-1)) .view(actions.size(0), -1) .sum(-1) .unsqueeze(-1) ) def mode(self): return self.probs.argmax(dim=-1, keepdim=True) # Normal class FixedNormal(torch.distributions.Normal): def log_probs(self, actions): return super().log_prob(actions).sum(-1, keepdim=True) def entrop(self): return super.entropy().sum(-1) def mode(self): return self.mean # Bernoulli class FixedBernoulli(torch.distributions.Bernoulli): def log_probs(self, actions): return super.log_prob(actions).view(actions.size(0), -1).sum(-1).unsqueeze(-1) def entropy(self): return super().entropy().sum(-1) def mode(self): return torch.gt(self.probs, 0.5).float() class Categorical(nn.Module): def __init__(self, num_inputs, num_outputs, use_orthogonal=True, gain=0.01): super(Categorical, self).__init__() init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain) self.linear = init_(nn.Linear(num_inputs, num_outputs)) def forward(self, x, available_actions=None): x = self.linear(x) if available_actions is not None: x[available_actions == 0] = -1e10 return FixedCategorical(logits=x) class DiagGaussian(nn.Module): def __init__(self, num_inputs, num_outputs, use_orthogonal=True, gain=0.01): super(DiagGaussian, self).__init__() init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain) self.fc_mean = init_(nn.Linear(num_inputs, num_outputs)) self.logstd = AddBias(torch.zeros(num_outputs)) def forward(self, x): action_mean = self.fc_mean(x) # An ugly hack for my KFAC implementation. zeros = torch.zeros(action_mean.size()) if x.is_cuda: zeros = zeros.cuda() action_logstd = self.logstd(zeros) return FixedNormal(action_mean, action_logstd.exp()) class Bernoulli(nn.Module): def __init__(self, num_inputs, num_outputs, use_orthogonal=True, gain=0.01): super(Bernoulli, self).__init__() init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain) self.linear = init_(nn.Linear(num_inputs, num_outputs)) def forward(self, x): x = self.linear(x) return FixedBernoulli(logits=x) class AddBias(nn.Module): def __init__(self, bias): super(AddBias, self).__init__() self._bias = nn.Parameter(bias.unsqueeze(1)) def forward(self, x): if x.dim() == 2: bias = self._bias.t().view(1, -1) else: bias = self._bias.t().view(1, -1, 1, 1) return x + bias

py

TiKick

TiKick-main/tmarl/networks/utils/mlp.py

import torch.nn as nn from .util import init, get_clones class MLPLayer(nn.Module): def __init__(self, input_dim, hidden_size, layer_N, use_orthogonal, activation_id): super(MLPLayer, self).__init__() self._layer_N = layer_N active_func = [nn.Tanh(), nn.ReLU(), nn.LeakyReLU(), nn.ELU()][activation_id] init_method = [nn.init.xavier_uniform_, nn.init.orthogonal_][use_orthogonal] gain = nn.init.calculate_gain(['tanh', 'relu', 'leaky_relu', 'leaky_relu'][activation_id]) def init_(m): return init(m, init_method, lambda x: nn.init.constant_(x, 0), gain=gain) self.fc1 = nn.Sequential( init_(nn.Linear(input_dim, hidden_size)), active_func, nn.LayerNorm(hidden_size)) self.fc_h = nn.Sequential(init_( nn.Linear(hidden_size, hidden_size)), active_func, nn.LayerNorm(hidden_size)) self.fc2 = get_clones(self.fc_h, self._layer_N) def forward(self, x): x = self.fc1(x) for i in range(self._layer_N): x = self.fc2[i](x) return x class MLPBase(nn.Module): def __init__(self, args, obs_shape, use_attn_internal=False, use_cat_self=True): super(MLPBase, self).__init__() self._use_feature_normalization = args.use_feature_normalization self._use_orthogonal = args.use_orthogonal self._activation_id = args.activation_id self._use_conv1d = args.use_conv1d self._stacked_frames = args.stacked_frames self._layer_N = args.layer_N self.hidden_size = args.hidden_size obs_dim = obs_shape[0] inputs_dim = obs_dim if self._use_feature_normalization: self.feature_norm = nn.LayerNorm(obs_dim) self.mlp = MLPLayer(inputs_dim, self.hidden_size, self._layer_N, self._use_orthogonal, self._activation_id) def forward(self, x): if self._use_feature_normalization: x = self.feature_norm(x) x = self.mlp(x) return x @property def output_size(self): return self.hidden_size

py

TiKick

TiKick-main/tmarl/networks/utils/popart.py

import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F class PopArt(torch.nn.Module): def __init__(self, input_shape, output_shape, norm_axes=1, beta=0.99999, epsilon=1e-5, device=torch.device("cpu")): super(PopArt, self).__init__() self.beta = beta self.epsilon = epsilon self.norm_axes = norm_axes self.tpdv = dict(dtype=torch.float32, device=device) self.input_shape = input_shape self.output_shape = output_shape self.weight = nn.Parameter(torch.Tensor(output_shape, input_shape)).to(**self.tpdv) self.bias = nn.Parameter(torch.Tensor(output_shape)).to(**self.tpdv) self.stddev = nn.Parameter(torch.ones(output_shape), requires_grad=False).to(**self.tpdv) self.mean = nn.Parameter(torch.zeros(output_shape), requires_grad=False).to(**self.tpdv) self.mean_sq = nn.Parameter(torch.zeros(output_shape), requires_grad=False).to(**self.tpdv) self.debiasing_term = nn.Parameter(torch.tensor(0.0), requires_grad=False).to(**self.tpdv) self.reset_parameters() def reset_parameters(self): torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) torch.nn.init.uniform_(self.bias, -bound, bound) self.mean.zero_() self.mean_sq.zero_() self.debiasing_term.zero_() def forward(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) return F.linear(input_vector, self.weight, self.bias) @torch.no_grad() def update(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) old_mean, old_stddev = self.mean, self.stddev batch_mean = input_vector.mean(dim=tuple(range(self.norm_axes))) batch_sq_mean = (input_vector ** 2).mean(dim=tuple(range(self.norm_axes))) self.mean.mul_(self.beta).add_(batch_mean * (1.0 - self.beta)) self.mean_sq.mul_(self.beta).add_(batch_sq_mean * (1.0 - self.beta)) self.debiasing_term.mul_(self.beta).add_(1.0 * (1.0 - self.beta)) self.stddev = (self.mean_sq - self.mean ** 2).sqrt().clamp(min=1e-4) self.weight = self.weight * old_stddev / self.stddev self.bias = (old_stddev * self.bias + old_mean - self.mean) / self.stddev def debiased_mean_var(self): debiased_mean = self.mean / self.debiasing_term.clamp(min=self.epsilon) debiased_mean_sq = self.mean_sq / self.debiasing_term.clamp(min=self.epsilon) debiased_var = (debiased_mean_sq - debiased_mean ** 2).clamp(min=1e-2) return debiased_mean, debiased_var def normalize(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.debiased_mean_var() out = (input_vector - mean[(None,) * self.norm_axes]) / torch.sqrt(var)[(None,) * self.norm_axes] return out def denormalize(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.debiased_mean_var() out = input_vector * torch.sqrt(var)[(None,) * self.norm_axes] + mean[(None,) * self.norm_axes] out = out.cpu().numpy() return out

py

TiKick

TiKick-main/tmarl/networks/utils/util.py

import copy import numpy as np import torch import torch.nn as nn def init(module, weight_init, bias_init, gain=1): weight_init(module.weight.data, gain=gain) bias_init(module.bias.data) return module def get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) def check(input): output = torch.from_numpy(input) if type(input) == np.ndarray else input return output

py

TiKick

TiKick-main/tmarl/networks/utils/act.py

from .distributions import Bernoulli, Categorical, DiagGaussian import torch import torch.nn as nn class ACTLayer(nn.Module): def __init__(self, action_space, inputs_dim, use_orthogonal, gain): super(ACTLayer, self).__init__() self.multidiscrete_action = False self.continuous_action = False self.mixed_action = False if action_space.__class__.__name__ == "Discrete": action_dim = action_space.n self.action_out = Categorical(inputs_dim, action_dim, use_orthogonal, gain) elif action_space.__class__.__name__ == "Box": self.continuous_action = True action_dim = action_space.shape[0] self.action_out = DiagGaussian(inputs_dim, action_dim, use_orthogonal, gain) elif action_space.__class__.__name__ == "MultiBinary": action_dim = action_space.shape[0] self.action_out = Bernoulli(inputs_dim, action_dim, use_orthogonal, gain) elif action_space.__class__.__name__ == "MultiDiscrete": self.multidiscrete_action = True action_dims = action_space.high - action_space.low + 1 self.action_outs = [] for action_dim in action_dims: self.action_outs.append(Categorical(inputs_dim, action_dim, use_orthogonal, gain)) self.action_outs = nn.ModuleList(self.action_outs) else: # discrete + continous self.mixed_action = True continous_dim = action_space[0].shape[0] discrete_dim = action_space[1].n self.action_outs = nn.ModuleList([DiagGaussian(inputs_dim, continous_dim, use_orthogonal, gain), Categorical( inputs_dim, discrete_dim, use_orthogonal, gain)]) def forward(self, x, available_actions=None, deterministic=False): if self.mixed_action : actions = [] action_log_probs = [] for action_out in self.action_outs: action_logit = action_out(x) action = action_logit.mode() if deterministic else action_logit.sample() action_log_prob = action_logit.log_probs(action) actions.append(action.float()) action_log_probs.append(action_log_prob) actions = torch.cat(actions, -1) action_log_probs = torch.sum(torch.cat(action_log_probs, -1), -1, keepdim=True) elif self.multidiscrete_action: actions = [] action_log_probs = [] for action_out in self.action_outs: action_logit = action_out(x) action = action_logit.mode() if deterministic else action_logit.sample() action_log_prob = action_logit.log_probs(action) actions.append(action) action_log_probs.append(action_log_prob) actions = torch.cat(actions, -1) action_log_probs = torch.cat(action_log_probs, -1) elif self.continuous_action: action_logits = self.action_out(x) actions = action_logits.mode() if deterministic else action_logits.sample() action_log_probs = action_logits.log_probs(actions) else: action_logits = self.action_out(x, available_actions) actions = action_logits.mode() if deterministic else action_logits.sample() action_log_probs = action_logits.log_probs(actions) return actions, action_log_probs def get_probs(self, x, available_actions=None): if self.mixed_action or self.multidiscrete_action: action_probs = [] for action_out in self.action_outs: action_logit = action_out(x) action_prob = action_logit.probs action_probs.append(action_prob) action_probs = torch.cat(action_probs, -1) elif self.continuous_action: action_logits = self.action_out(x) action_probs = action_logits.probs else: action_logits = self.action_out(x, available_actions) action_probs = action_logits.probs return action_probs def get_log_1mp(self, x, action, available_actions=None, active_masks=None): action_logits = self.action_out(x, available_actions) action_prob = torch.gather(action_logits.probs, 1, action.long()) action_prob = torch.clamp(action_prob, 0, 1-1e-6) action_log_1mp = torch.log(1 - action_prob) return action_log_1mp def evaluate_actions(self, x, action, available_actions=None, active_masks=None): if self.mixed_action: a, b = action.split((2, 1), -1) b = b.long() action = [a, b] action_log_probs = [] dist_entropy = [] for action_out, act in zip(self.action_outs, action): action_logit = action_out(x) action_log_probs.append(action_logit.log_probs(act)) if active_masks is not None: if len(action_logit.entropy().shape) == len(active_masks.shape): dist_entropy.append((action_logit.entropy() * active_masks).sum()/active_masks.sum()) else: dist_entropy.append((action_logit.entropy() * active_masks.squeeze(-1)).sum()/active_masks.sum()) else: dist_entropy.append(action_logit.entropy().mean()) action_log_probs = torch.sum(torch.cat(action_log_probs, -1), -1, keepdim=True) dist_entropy = dist_entropy[0] * 0.0025 + dist_entropy[1] * 0.01 elif self.multidiscrete_action: action = torch.transpose(action, 0, 1) action_log_probs = [] dist_entropy = [] for action_out, act in zip(self.action_outs, action): action_logit = action_out(x) action_log_probs.append(action_logit.log_probs(act)) if active_masks is not None: dist_entropy.append((action_logit.entropy()*active_masks.squeeze(-1)).sum()/active_masks.sum()) else: dist_entropy.append(action_logit.entropy().mean()) action_log_probs = torch.cat(action_log_probs, -1) # ! could be wrong dist_entropy = torch.tensor(dist_entropy).mean() elif self.continuous_action: action_logits = self.action_out(x) action_log_probs = action_logits.log_probs(action) if active_masks is not None: dist_entropy = (action_logits.entropy()*active_masks).sum()/active_masks.sum() else: dist_entropy = action_logits.entropy().mean() else: action_logits = self.action_out(x, available_actions) action_log_probs = action_logits.log_probs(action) if active_masks is not None: dist_entropy = (action_logits.entropy()*active_masks.squeeze(-1)).sum()/active_masks.sum() else: dist_entropy = action_logits.entropy().mean() return action_log_probs, dist_entropy

py

TiKick

TiKick-main/tmarl/networks/utils/rnn.py

import torch import torch.nn as nn class RNNLayer(nn.Module): def __init__(self, inputs_dim, outputs_dim, recurrent_N, use_orthogonal): super(RNNLayer, self).__init__() self._recurrent_N = recurrent_N self._use_orthogonal = use_orthogonal self.rnn = nn.GRU(inputs_dim, outputs_dim, num_layers=self._recurrent_N) for name, param in self.rnn.named_parameters(): if 'bias' in name: nn.init.constant_(param, 0) elif 'weight' in name: if self._use_orthogonal: nn.init.orthogonal_(param) else: nn.init.xavier_uniform_(param) self.norm = nn.LayerNorm(outputs_dim) def forward(self, x, hxs, masks): if x.size(0) == hxs.size(0): x, hxs = self.rnn(x.unsqueeze(0), (hxs * masks.repeat(1, self._recurrent_N).unsqueeze(-1)).transpose(0, 1).contiguous()) x = x.squeeze(0) hxs = hxs.transpose(0, 1) else: # x is a (T, N, -1) tensor that has been flatten to (T * N, -1) N = hxs.size(0) T = int(x.size(0) / N) # unflatten x = x.view(T, N, x.size(1)) # Same deal with masks masks = masks.view(T, N) # Let's figure out which steps in the sequence have a zero for any agent # We will always assume t=0 has a zero in it as that makes the logic cleaner has_zeros = ((masks[1:] == 0.0) .any(dim=-1) .nonzero() .squeeze() .cpu()) # +1 to correct the masks[1:] if has_zeros.dim() == 0: # Deal with scalar has_zeros = [has_zeros.item() + 1] else: has_zeros = (has_zeros + 1).numpy().tolist() # add t=0 and t=T to the list has_zeros = [0] + has_zeros + [T] hxs = hxs.transpose(0, 1) outputs = [] for i in range(len(has_zeros) - 1): # We can now process steps that don't have any zeros in masks together! # This is much faster start_idx = has_zeros[i] end_idx = has_zeros[i + 1] temp = (hxs * masks[start_idx].view(1, -1, 1).repeat(self._recurrent_N, 1, 1)).contiguous() rnn_scores, hxs = self.rnn(x[start_idx:end_idx], temp) outputs.append(rnn_scores) # assert len(outputs) == T # x is a (T, N, -1) tensor x = torch.cat(outputs, dim=0) # flatten x = x.reshape(T * N, -1) hxs = hxs.transpose(0, 1) x = self.norm(x) return x, hxs

py

TiKick

TiKick-main/tmarl/drivers/shared_distributed/base_driver.py

import numpy as np import torch def _t2n(x): return x.detach().cpu().numpy() class Driver(object): def __init__(self, config, client=None): self.all_args = config['all_args'] self.envs = config['envs'] self.eval_envs = config['eval_envs'] self.device = config['device'] self.num_agents = config['num_agents'] if 'signal' in config: self.actor_id = config['signal'].actor_id self.weight_ids = config['signal'].weight_ids else: self.actor_id = 0 self.weight_ids = [0] # parameters self.env_name = self.all_args.env_name self.algorithm_name = self.all_args.algorithm_name self.experiment_name = self.all_args.experiment_name self.use_centralized_V = self.all_args.use_centralized_V self.use_obs_instead_of_state = self.all_args.use_obs_instead_of_state self.num_env_steps = self.all_args.num_env_steps if hasattr(self.all_args,'num_env_steps') else self.all_args.eval_num self.episode_length = self.all_args.episode_length self.n_rollout_threads = self.all_args.n_rollout_threads self.learner_n_rollout_threads = self.all_args.n_rollout_threads self.n_eval_rollout_threads = self.all_args.n_eval_rollout_threads self.hidden_size = self.all_args.hidden_size self.recurrent_N = self.all_args.recurrent_N # interval self.save_interval = self.all_args.save_interval self.use_eval = self.all_args.use_eval self.eval_interval = self.all_args.eval_interval self.log_interval = self.all_args.log_interval # dir self.model_dir = self.all_args.model_dir if self.algorithm_name == "rmappo": from tmarl.algorithms.r_mappo_distributed.mappo_algorithm import MAPPOAlgorithm as TrainAlgo from tmarl.algorithms.r_mappo_distributed.mappo_module import MAPPOModule as AlgoModule else: raise NotImplementedError if self.envs: share_observation_space = self.envs.share_observation_space[0] \ if self.use_centralized_V else self.envs.observation_space[0] # policy network self.algo_module = AlgoModule(self.all_args, self.envs.observation_space[0], share_observation_space, self.envs.action_space[0], device=self.device) else: share_observation_space = self.eval_envs.share_observation_space[0] \ if self.use_centralized_V else self.eval_envs.observation_space[0] # policy network self.algo_module = AlgoModule(self.all_args, self.eval_envs.observation_space[0], share_observation_space, self.eval_envs.action_space[0], device=self.device) if self.model_dir is not None: self.restore() # algorithm self.trainer = TrainAlgo(self.all_args, self.algo_module, device=self.device) # buffer from tmarl.replay_buffers.normal.shared_buffer import SharedReplayBuffer self.buffer = SharedReplayBuffer(self.all_args, self.num_agents, self.envs.observation_space[0] if self.envs else self.eval_envs.observation_space[0], share_observation_space, self.envs.action_space[0] if self.envs else self.eval_envs.action_space[0]) def run(self): raise NotImplementedError def warmup(self): raise NotImplementedError def collect(self, step): raise NotImplementedError def insert(self, data): raise NotImplementedError def restore(self): policy_actor_state_dict = torch.load(str(self.model_dir) + '/actor.pt', map_location=self.device) self.algo_module.actor.load_state_dict(policy_actor_state_dict)

py

TiKick

TiKick-main/tmarl/drivers/shared_distributed/football_driver.py

from tqdm import tqdm import numpy as np from tmarl.drivers.shared_distributed.base_driver import Driver def _t2n(x): return x.detach().cpu().numpy() class FootballDriver(Driver): def __init__(self, config): super(FootballDriver, self).__init__(config) def run(self): self.trainer.prep_rollout() episodes = int(self.num_env_steps) total_num_steps = 0 for episode in range(episodes): print('Episode {}:'.format(episode)) self.eval(total_num_steps) def eval(self, total_num_steps): eval_episode_rewards = [] eval_obs, eval_share_obs, eval_available_actions = self.eval_envs.reset() agent_num = eval_obs.shape[1] used_buffer = self.buffer rnn_shape = [self.n_eval_rollout_threads, agent_num, *used_buffer.rnn_states_critic.shape[3:]] eval_rnn_states = np.zeros(rnn_shape, dtype=np.float32) eval_rnn_states_critic = np.zeros(rnn_shape, dtype=np.float32) eval_masks = np.ones((self.n_eval_rollout_threads, agent_num, 1), dtype=np.float32) finished = None for eval_step in tqdm(range(3001)): self.trainer.prep_rollout() _, eval_action, eval_action_log_prob, eval_rnn_states, _ = \ self.trainer.algo_module.get_actions(np.concatenate(eval_share_obs), np.concatenate(eval_obs), np.concatenate(eval_rnn_states), None, np.concatenate(eval_masks), np.concatenate(eval_available_actions), deterministic=True) eval_actions = np.array( np.split(_t2n(eval_action), self.n_eval_rollout_threads)) eval_rnn_states = np.array( np.split(_t2n(eval_rnn_states), self.n_eval_rollout_threads)) if self.eval_envs.action_space[0].__class__.__name__ == 'Discrete': eval_actions_env = np.squeeze( np.eye(self.eval_envs.action_space[0].n)[eval_actions], 2) else: raise NotImplementedError # Obser reward and next obs eval_obs, eval_share_obs, eval_rewards, eval_dones, eval_infos, eval_available_actions = \ self.eval_envs.step(eval_actions_env) eval_rewards = eval_rewards.reshape([-1, agent_num]) # [roll_out, num_agents] if finished is None: eval_r = eval_rewards[:,:self.num_agents] eval_episode_rewards.append(eval_r) finished = eval_dones.copy() else: eval_r = (eval_rewards * ~finished)[:,:self.num_agents] eval_episode_rewards.append(eval_r) finished = eval_dones.copy() | finished eval_masks = np.ones( (self.n_eval_rollout_threads, agent_num, 1), dtype=np.float32) eval_masks[eval_dones == True] = np.zeros( ((eval_dones == True).sum(), 1), dtype=np.float32) eval_rnn_states[eval_dones == True] = np.zeros( ((eval_dones == True).sum(), self.recurrent_N, self.hidden_size), dtype=np.float32) if finished.all() == True: break eval_episode_rewards = np.array(eval_episode_rewards) # [step,rollout,num_agents] ally_goal = np.sum((eval_episode_rewards == 1), axis=0) enemy_goal = np.sum((eval_episode_rewards == -1), axis=0) net_goal = np.sum(eval_episode_rewards, axis=0) winning_rate = np.mean(net_goal, axis=-1) eval_env_infos = {} eval_env_infos['eval_average_winning_rate'] = winning_rate>0 eval_env_infos['eval_average_losing_rate'] = winning_rate<0 eval_env_infos['eval_average_draw_rate'] = winning_rate==0 eval_env_infos['eval_average_ally_score'] = ally_goal eval_env_infos['eval_average_enemy_score'] = enemy_goal eval_env_infos['eval_average_net_score'] = net_goal print("\tSuccess Rate: " + str(np.mean(winning_rate>0)) )

py

TiKick

TiKick-main/tmarl/algorithms/r_mappo_distributed/mappo_algorithm.py

import torch from tmarl.utils.valuenorm import ValueNorm # implement the loss of the MAPPO here class MAPPOAlgorithm(): def __init__(self, args, init_module, device=torch.device("cpu")): self.device = device self.tpdv = dict(dtype=torch.float32, device=device) self.algo_module = init_module self.clip_param = args.clip_param self.ppo_epoch = args.ppo_epoch self.num_mini_batch = args.num_mini_batch self.data_chunk_length = args.data_chunk_length self.policy_value_loss_coef = args.policy_value_loss_coef self.value_loss_coef = args.value_loss_coef self.entropy_coef = args.entropy_coef self.max_grad_norm = args.max_grad_norm self.huber_delta = args.huber_delta self._use_recurrent_policy = args.use_recurrent_policy self._use_naive_recurrent = args.use_naive_recurrent_policy self._use_max_grad_norm = args.use_max_grad_norm self._use_clipped_value_loss = args.use_clipped_value_loss self._use_huber_loss = args.use_huber_loss self._use_popart = args.use_popart self._use_valuenorm = args.use_valuenorm self._use_value_active_masks = args.use_value_active_masks self._use_policy_active_masks = args.use_policy_active_masks self._use_policy_vhead = args.use_policy_vhead assert (self._use_popart and self._use_valuenorm) == False, ("self._use_popart and self._use_valuenorm can not be set True simultaneously") if self._use_popart: self.value_normalizer = self.algo_module.critic.v_out if self._use_policy_vhead: self.policy_value_normalizer = self.algo_module.actor.v_out elif self._use_valuenorm: self.value_normalizer = ValueNorm(1, device = self.device) if self._use_policy_vhead: self.policy_value_normalizer = ValueNorm(1, device = self.device) else: self.value_normalizer = None if self._use_policy_vhead: self.policy_value_normalizer = None def prep_rollout(self): self.algo_module.actor.eval()

py

TiKick

TiKick-main/tmarl/algorithms/r_mappo_distributed/mappo_module.py

import torch from tmarl.networks.policy_network import PolicyNetwork class MAPPOModule: def __init__(self, args, obs_space, share_obs_space, act_space, device=torch.device("cpu")): self.device = device self.lr = args.lr self.critic_lr = args.critic_lr self.opti_eps = args.opti_eps self.weight_decay = args.weight_decay self.obs_space = obs_space self.share_obs_space = share_obs_space self.act_space = act_space self.actor = PolicyNetwork(args, self.obs_space, self.act_space, self.device) self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=self.lr, eps=self.opti_eps, weight_decay=self.weight_decay) def get_actions(self, share_obs, obs, rnn_states_actor, rnn_states_critic, masks, available_actions=None, deterministic=False): actions, action_log_probs, rnn_states_actor = self.actor(obs, rnn_states_actor, masks, available_actions, deterministic) return None, actions, action_log_probs, rnn_states_actor, None

py

TiKick

TiKick-main/tmarl/loggers/utils.py

import time def timer(function): """ 装饰器函数timer :param function:想要计时的函数 :return: """ def wrapper(*args, **kwargs): time_start = time.time() res = function(*args, **kwargs) cost_time = time.time() - time_start print("{} running time: {}s".format(function.__name__, cost_time)) return res return wrapper

py

TiKick

TiKick-main/tmarl/runners/base_evaluator.py

import random import numpy as np import torch from tmarl.configs.config import get_config from tmarl.runners.base_runner import Runner def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) class Evaluator(Runner): def __init__(self, argv,program_type=None, client=None): super().__init__(argv) parser = get_config() all_args = self.extra_args_func(argv, parser) all_args.cuda = not all_args.disable_cuda self.algorithm_name = all_args.algorithm_name # cuda if not all_args.disable_cuda and torch.cuda.is_available(): device = torch.device("cuda:0") if all_args.cuda_deterministic: torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True else: print("choose to use cpu...") device = torch.device("cpu") # run dir run_dir = self.setup_run_dir(all_args) # env init Env_Class, SubprocVecEnv, DummyVecEnv = self.get_env() eval_envs = self.env_init( all_args, Env_Class, SubprocVecEnv, DummyVecEnv) num_agents = all_args.num_agents config = { "all_args": all_args, "envs": None, "eval_envs": eval_envs, "num_agents": num_agents, "device": device, "run_dir": run_dir, } self.all_args, self.envs, self.eval_envs, self.config \ = all_args, None, eval_envs, config self.driver = self.init_driver() def run(self): # run experiments self.driver.run() self.stop() def stop(self): pass def extra_args_func(self, argv, parser): raise NotImplementedError def get_env(self): raise NotImplementedError def init_driver(self): raise NotImplementedError def make_eval_env(self, all_args, Env_Class, SubprocVecEnv, DummyVecEnv): def get_env_fn(rank): def init_env(): env = Env_Class(all_args) env.seed(all_args.seed * 50000 + rank * 10000) return env return init_env if all_args.n_eval_rollout_threads == 1: return DummyVecEnv([get_env_fn(0)]) else: return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_eval_rollout_threads)]) def env_init(self, all_args, Env_Class, SubprocVecEnv, DummyVecEnv): eval_envs = self.make_eval_env( all_args, Env_Class, SubprocVecEnv, DummyVecEnv) if all_args.use_eval else None return eval_envs def setup_run_dir(self, all_args): return None

py

TiKick

TiKick-main/tmarl/runners/base_runner.py

import os import random import socket import setproctitle import numpy as np from pathlib import Path import torch from tmarl.configs.config import get_config def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) class Runner: def __init__(self, argv): self.argv = argv def run(self): # main run raise NotImplementedError

py

TiKick

TiKick-main/tmarl/runners/football/football_evaluator.py

import sys import os from pathlib import Path from tmarl.runners.base_evaluator import Evaluator from tmarl.envs.football.football import RllibGFootball from tmarl.envs.env_wrappers import ShareSubprocVecEnv, ShareDummyVecEnv class FootballEvaluator(Evaluator): def __init__(self, argv): super(FootballEvaluator, self).__init__(argv) def setup_run_dir(self, all_args): dump_dir = Path(all_args.replay_save_dir) if not dump_dir.exists(): os.makedirs(str(dump_dir)) self.dump_dir = dump_dir return super(FootballEvaluator, self).setup_run_dir(all_args) def make_eval_env(self, all_args, Env_Class, SubprocVecEnv, DummyVecEnv): def get_env_fn(rank): def init_env(): env = Env_Class(all_args, rank, log_dir=str(self.dump_dir), isEval=True) env.seed(all_args.seed * 50000 + rank * 10000) return env return init_env if all_args.n_eval_rollout_threads == 1: return DummyVecEnv([get_env_fn(0)]) else: return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_eval_rollout_threads)]) def extra_args_func(self, args, parser): parser.add_argument('--scenario_name', type=str, default='simple_spread', help="Which scenario to run on") parser.add_argument('--num_agents', type=int, default=0, help="number of players") # football config parser.add_argument('--representation', type=str, default='raw', help="format of the observation in gfootball env") parser.add_argument('--rewards', type=str, default='scoring', help="format of the reward in gfootball env") parser.add_argument("--render_only", action='store_true', default=False, help="if ture, render without training") all_args = parser.parse_known_args(args)[0] return all_args def get_env(self): return RllibGFootball, ShareSubprocVecEnv, ShareDummyVecEnv def init_driver(self): if not self.all_args.separate_policy: from tmarl.drivers.shared_distributed.football_driver import FootballDriver as Driver else: raise NotImplementedError driver = Driver(self.config) return driver def main(argv): evaluator = FootballEvaluator(argv) evaluator.run() if __name__ == "__main__": main(sys.argv[1:])

py

TiKick

TiKick-main/tmarl/utils/multi_discrete.py

import gym import numpy as np # An old version of OpenAI Gym's multi_discrete.py. (Was getting affected by Gym updates) class MultiDiscrete(gym.Space): """ - The multi-discrete action space consists of a series of discrete action spaces with different parameters - It can be adapted to both a Discrete action space or a continuous (Box) action space - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space - It is parametrized by passing an array of arrays containing [min, max] for each discrete action space where the discrete action space can take any integers from `min` to `max` (both inclusive) Note: A value of 0 always need to represent the NOOP action. e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces: 1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4 2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 - Can be initialized as MultiDiscrete([ [0,4], [0,1], [0,1] ]) """ def __init__(self, array_of_param_array): self.low = np.array([x[0] for x in array_of_param_array]) self.high = np.array([x[1] for x in array_of_param_array]) self.num_discrete_space = self.low.shape[0] self.n = np.sum(self.high) + 2 def sample(self): """ Returns a array with one sample from each discrete action space """ # For each row: round(random .* (max - min) + min, 0) random_array = np.random.rand(self.num_discrete_space) return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)] def contains(self, x): return len(x) == self.num_discrete_space and (np.array(x) >= self.low).all() and (np.array(x) <= self.high).all() @property def shape(self): return self.num_discrete_space def __repr__(self): return "MultiDiscrete" + str(self.num_discrete_space) def __eq__(self, other): return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high)

py

TiKick

TiKick-main/tmarl/utils/valuenorm.py

import numpy as np import torch import torch.nn as nn class ValueNorm(nn.Module): """ Normalize a vector of observations - across the first norm_axes dimensions""" def __init__(self, input_shape, norm_axes=1, beta=0.99999, per_element_update=False, epsilon=1e-5, device=torch.device("cpu")): super(ValueNorm, self).__init__() self.input_shape = input_shape self.norm_axes = norm_axes self.epsilon = epsilon self.beta = beta self.per_element_update = per_element_update self.tpdv = dict(dtype=torch.float32, device=device) self.running_mean = nn.Parameter(torch.zeros(input_shape), requires_grad=False).to(**self.tpdv) self.running_mean_sq = nn.Parameter(torch.zeros(input_shape), requires_grad=False).to(**self.tpdv) self.debiasing_term = nn.Parameter(torch.tensor(0.0), requires_grad=False).to(**self.tpdv) self.reset_parameters() def reset_parameters(self): self.running_mean.zero_() self.running_mean_sq.zero_() self.debiasing_term.zero_() def running_mean_var(self): debiased_mean = self.running_mean / self.debiasing_term.clamp(min=self.epsilon) debiased_mean_sq = self.running_mean_sq / self.debiasing_term.clamp(min=self.epsilon) debiased_var = (debiased_mean_sq - debiased_mean ** 2).clamp(min=1e-2) return debiased_mean, debiased_var @torch.no_grad() def update(self, input_vector): if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) batch_mean = input_vector.mean(dim=tuple(range(self.norm_axes))) batch_sq_mean = (input_vector ** 2).mean(dim=tuple(range(self.norm_axes))) if self.per_element_update: batch_size = np.prod(input_vector.size()[:self.norm_axes]) weight = self.beta ** batch_size else: weight = self.beta self.running_mean.mul_(weight).add_(batch_mean * (1.0 - weight)) self.running_mean_sq.mul_(weight).add_(batch_sq_mean * (1.0 - weight)) self.debiasing_term.mul_(weight).add_(1.0 * (1.0 - weight)) def normalize(self, input_vector): # Make sure input is float32 if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.running_mean_var() out = (input_vector - mean[(None,) * self.norm_axes]) / torch.sqrt(var)[(None,) * self.norm_axes] return out def denormalize(self, input_vector): """ Transform normalized data back into original distribution """ if type(input_vector) == np.ndarray: input_vector = torch.from_numpy(input_vector) input_vector = input_vector.to(**self.tpdv) mean, var = self.running_mean_var() out = input_vector * torch.sqrt(var)[(None,) * self.norm_axes] + mean[(None,) * self.norm_axes] out = out.cpu().numpy() return out

py

TiKick

TiKick-main/tmarl/utils/util.py

import copy import numpy as np import math import gym import torch import torch.nn as nn import torch.nn.functional as F import torch.distributed as dist from torch.autograd import Variable from gym.spaces import Box, Discrete, Tuple def check(input): if type(input) == np.ndarray: return torch.from_numpy(input) def get_gard_norm(it): sum_grad = 0 for x in it: if x.grad is None: continue sum_grad += x.grad.norm() ** 2 return math.sqrt(sum_grad) def update_linear_schedule(optimizer, epoch, total_num_epochs, initial_lr): """Decreases the learning rate linearly""" lr = initial_lr - (initial_lr * (epoch / float(total_num_epochs))) for param_group in optimizer.param_groups: param_group['lr'] = lr def huber_loss(e, d): a = (abs(e) <= d).float() b = (e > d).float() return a*e**2/2 + b*d*(abs(e)-d/2) def mse_loss(e): return e**2/2 def get_shape_from_obs_space(obs_space): if obs_space.__class__.__name__ == 'Box': obs_shape = obs_space.shape elif obs_space.__class__.__name__ == 'list': obs_shape = obs_space elif obs_space.__class__.__name__ == 'Dict': obs_shape = obs_space.spaces else: raise NotImplementedError return obs_shape def get_shape_from_act_space(act_space): if act_space.__class__.__name__ == 'Discrete': act_shape = 1 elif act_space.__class__.__name__ == "MultiDiscrete": act_shape = act_space.shape elif act_space.__class__.__name__ == "Box": act_shape = act_space.shape[0] elif act_space.__class__.__name__ == "MultiBinary": act_shape = act_space.shape[0] else: # agar act_shape = act_space[0].shape[0] + 1 return act_shape def tile_images(img_nhwc): """ Tile N images into one big PxQ image (P,Q) are chosen to be as close as possible, and if N is square, then P=Q. input: img_nhwc, list or array of images, ndim=4 once turned into array n = batch index, h = height, w = width, c = channel returns: bigim_HWc, ndarray with ndim=3 """ img_nhwc = np.asarray(img_nhwc) N, h, w, c = img_nhwc.shape H = int(np.ceil(np.sqrt(N))) W = int(np.ceil(float(N)/H)) img_nhwc = np.array( list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)]) img_HWhwc = img_nhwc.reshape(H, W, h, w, c) img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4) img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c) return img_Hh_Ww_c def to_torch(input): return torch.from_numpy(input) if type(input) == np.ndarray else input def to_numpy(x): return x.detach().cpu().numpy() class FixedCategorical(torch.distributions.Categorical): def sample(self): return super().sample() def log_probs(self, actions): return ( super() .log_prob(actions.squeeze(-1)) .view(actions.size(0), -1) .sum(-1) .unsqueeze(-1) ) def mode(self): return self.probs.argmax(dim=-1, keepdim=True) class MultiDiscrete(gym.Space): """ - The multi-discrete action space consists of a series of discrete action spaces with different parameters - It can be adapted to both a Discrete action space or a continuous (Box) action space - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space - It is parametrized by passing an array of arrays containing [min, max] for each discrete action space where the discrete action space can take any integers from `min` to `max` (both inclusive) Note: A value of 0 always need to represent the NOOP action. e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces: 1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4 2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1 - Can be initialized as MultiDiscrete([ [0,4], [0,1], [0,1] ]) """ def __init__(self, array_of_param_array): self.low = np.array([x[0] for x in array_of_param_array]) self.high = np.array([x[1] for x in array_of_param_array]) self.num_discrete_space = self.low.shape[0] self.n = np.sum(self.high) + 2 def sample(self): """ Returns a array with one sample from each discrete action space """ # For each row: round(random .* (max - min) + min, 0) random_array = np.random.rand(self.num_discrete_space) return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)] def contains(self, x): return len(x) == self.num_discrete_space and (np.array(x) >= self.low).all() and (np.array(x) <= self.high).all() @property def shape(self): return self.num_discrete_space def __repr__(self): return "MultiDiscrete" + str(self.num_discrete_space) def __eq__(self, other): return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high) class DecayThenFlatSchedule(): def __init__(self, start, finish, time_length, decay="exp"): self.start = start self.finish = finish self.time_length = time_length self.delta = (self.start - self.finish) / self.time_length self.decay = decay if self.decay in ["exp"]: self.exp_scaling = (-1) * self.time_length / \ np.log(self.finish) if self.finish > 0 else 1 def eval(self, T): if self.decay in ["linear"]: return max(self.finish, self.start - self.delta * T) elif self.decay in ["exp"]: return min(self.start, max(self.finish, np.exp(- T / self.exp_scaling))) pass def huber_loss(e, d): a = (abs(e) <= d).float() b = (e > d).float() return a*e**2/2 + b*d*(abs(e)-d/2) def mse_loss(e): return e**2 def init(module, weight_init, bias_init, gain=1): weight_init(module.weight.data, gain=gain) bias_init(module.bias.data) return module def get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) def soft_update(target, source, tau): """ Perform DDPG soft update (move target params toward source based on weight factor tau) Inputs: target (torch.nn.Module): Net to copy parameters to source (torch.nn.Module): Net whose parameters to copy tau (float, 0 < x < 1): Weight factor for update """ for target_param, param in zip(target.parameters(), source.parameters()): target_param.data.copy_( target_param.data * (1.0 - tau) + param.data * tau) def hard_update(target, source): """ Copy network parameters from source to target Inputs: target (torch.nn.Module): Net to copy parameters to source (torch.nn.Module): Net whose parameters to copy """ for target_param, param in zip(target.parameters(), source.parameters()): target_param.data.copy_(param.data) def average_gradients(model): """ Gradient averaging. """ size = float(dist.get_world_size()) for param in model.parameters(): dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM, group=0) param.grad.data /= size def onehot_from_logits(logits, avail_logits=None, eps=0.0): """ Given batch of logits, return one-hot sample using epsilon greedy strategy (based on given epsilon) """ # get best (according to current policy) actions in one-hot form logits = to_torch(logits) dim = len(logits.shape) - 1 if avail_logits is not None: avail_logits = to_torch(avail_logits) logits[avail_logits == 0] = -1e10 argmax_acs = (logits == logits.max(dim, keepdim=True)[0]).float() if eps == 0.0: return argmax_acs # get random actions in one-hot form rand_acs = Variable(torch.eye(logits.shape[1])[[np.random.choice( range(logits.shape[1]), size=logits.shape[0])]], requires_grad=False) # chooses between best and random actions using epsilon greedy return torch.stack([argmax_acs[i] if r > eps else rand_acs[i] for i, r in enumerate(torch.rand(logits.shape[0]))]) def sample_gumbel(shape, eps=1e-20, tens_type=torch.FloatTensor): """Sample from Gumbel(0, 1)""" U = Variable(tens_type(*shape).uniform_(), requires_grad=False) return -torch.log(-torch.log(U + eps) + eps) def gumbel_softmax_sample(logits, avail_logits, temperature, device=torch.device('cpu')): """ Draw a sample from the Gumbel-Softmax distribution""" if str(device) == 'cpu': y = logits + sample_gumbel(logits.shape, tens_type=type(logits.data)) else: y = (logits.cpu() + sample_gumbel(logits.shape, tens_type=type(logits.data))).cuda() dim = len(logits.shape) - 1 if avail_logits is not None: avail_logits = to_torch(avail_logits).to(device) y[avail_logits == 0] = -1e10 return F.softmax(y / temperature, dim=dim) def gumbel_softmax(logits, avail_logits=None, temperature=1.0, hard=False, device=torch.device('cpu')): """Sample from the Gumbel-Softmax distribution and optionally discretize. Args: logits: [batch_size, n_class] unnormalized log-probs temperature: non-negative scalar hard: if True, take argmax, but differentiate w.r.t. soft sample y Returns: [batch_size, n_class] sample from the Gumbel-Softmax distribution. If hard=True, then the returned sample will be one-hot, otherwise it will be a probabilitiy distribution that sums to 1 across classes """ y = gumbel_softmax_sample(logits, avail_logits, temperature, device) if hard: y_hard = onehot_from_logits(y) y = (y_hard - y).detach() + y return y def gaussian_noise(shape, std): return torch.empty(shape).normal_(mean=0, std=std) def get_obs_shape(obs_space): if obs_space.__class__.__name__ == "Box": obs_shape = obs_space.shape elif obs_space.__class__.__name__ == "list": obs_shape = obs_space else: raise NotImplementedError return obs_shape def get_dim_from_space(space): if isinstance(space, Box): dim = space.shape[0] elif isinstance(space, Discrete): dim = space.n elif isinstance(space, Tuple): dim = sum([get_dim_from_space(sp) for sp in space]) elif "MultiDiscrete" in space.__class__.__name__: return (space.high - space.low) + 1 elif isinstance(space, list): dim = space[0] else: raise Exception("Unrecognized space: ", type(space)) return dim def get_state_dim(observation_dict, action_dict): combined_obs_dim = sum([get_dim_from_space(space) for space in observation_dict.values()]) combined_act_dim = 0 for space in action_dict.values(): dim = get_dim_from_space(space) if isinstance(dim, np.ndarray): combined_act_dim += int(sum(dim)) else: combined_act_dim += dim return combined_obs_dim, combined_act_dim, combined_obs_dim+combined_act_dim def get_cent_act_dim(action_space): cent_act_dim = 0 for space in action_space: dim = get_dim_from_space(space) if isinstance(dim, np.ndarray): cent_act_dim += int(sum(dim)) else: cent_act_dim += dim return cent_act_dim def is_discrete(space): if isinstance(space, Discrete) or "MultiDiscrete" in space.__class__.__name__: return True else: return False def is_multidiscrete(space): if "MultiDiscrete" in space.__class__.__name__: return True else: return False def make_onehot(int_action, action_dim, seq_len=None): if type(int_action) == torch.Tensor: int_action = int_action.cpu().numpy() if not seq_len: return np.eye(action_dim)[int_action] if seq_len: onehot_actions = [] for i in range(seq_len): onehot_action = np.eye(action_dim)[int_action[i]] onehot_actions.append(onehot_action) return np.stack(onehot_actions) def avail_choose(x, avail_x=None): x = to_torch(x) if avail_x is not None: avail_x = to_torch(avail_x) x[avail_x == 0] = -1e10 return x # FixedCategorical(logits=x) def tile_images(img_nhwc): """ Tile N images into one big PxQ image (P,Q) are chosen to be as close as possible, and if N is square, then P=Q. input: img_nhwc, list or array of images, ndim=4 once turned into array n = batch index, h = height, w = width, c = channel returns: bigim_HWc, ndarray with ndim=3 """ img_nhwc = np.asarray(img_nhwc) N, h, w, c = img_nhwc.shape H = int(np.ceil(np.sqrt(N))) W = int(np.ceil(float(N)/H)) img_nhwc = np.array( list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)]) img_HWhwc = img_nhwc.reshape(H, W, h, w, c) img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4) img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c) return img_Hh_Ww_c

py

TiKick

TiKick-main/tmarl/utils/segment_tree.py

import numpy as np def unique(sorted_array): """ More efficient implementation of np.unique for sorted arrays :param sorted_array: (np.ndarray) :return:(np.ndarray) sorted_array without duplicate elements """ if len(sorted_array) == 1: return sorted_array left = sorted_array[:-1] right = sorted_array[1:] uniques = np.append(right != left, True) return sorted_array[uniques] class SegmentTree(object): def __init__(self, capacity, operation, neutral_element): """ Build a Segment Tree data structure. https://en.wikipedia.org/wiki/Segment_tree Can be used as regular array that supports Index arrays, but with two important differences: a) setting item's value is slightly slower. It is O(lg capacity) instead of O(1). b) user has access to an efficient ( O(log segment size) ) `reduce` operation which reduces `operation` over a contiguous subsequence of items in the array. :param capacity: (int) Total size of the array - must be a power of two. :param operation: (lambda (Any, Any): Any) operation for combining elements (eg. sum, max) must form a mathematical group together with the set of possible values for array elements (i.e. be associative) :param neutral_element: (Any) neutral element for the operation above. eg. float('-inf') for max and 0 for sum. """ assert capacity > 0 and capacity & ( capacity - 1) == 0, "capacity must be positive and a power of 2." self._capacity = capacity self._value = [neutral_element for _ in range(2 * capacity)] self._operation = operation self.neutral_element = neutral_element def _reduce_helper(self, start, end, node, node_start, node_end): if start == node_start and end == node_end: return self._value[node] mid = (node_start + node_end) // 2 if end <= mid: return self._reduce_helper(start, end, 2 * node, node_start, mid) else: if mid + 1 <= start: return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) else: return self._operation( self._reduce_helper(start, mid, 2 * node, node_start, mid), self._reduce_helper( mid + 1, end, 2 * node + 1, mid + 1, node_end) ) def reduce(self, start=0, end=None): """ Returns result of applying `self.operation` to a contiguous subsequence of the array. self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) :param start: (int) beginning of the subsequence :param end: (int) end of the subsequences :return: (Any) result of reducing self.operation over the specified range of array elements. """ if end is None: end = self._capacity if end < 0: end += self._capacity end -= 1 return self._reduce_helper(start, end, 1, 0, self._capacity - 1) def __setitem__(self, idx, val): # indexes of the leaf idxs = idx + self._capacity self._value[idxs] = val if isinstance(idxs, int): idxs = np.array([idxs]) # go up one level in the tree and remove duplicate indexes idxs = unique(idxs // 2) while len(idxs) > 1 or idxs[0] > 0: # as long as there are non-zero indexes, update the corresponding values self._value[idxs] = self._operation( self._value[2 * idxs], self._value[2 * idxs + 1] ) # go up one level in the tree and remove duplicate indexes idxs = unique(idxs // 2) def __getitem__(self, idx): assert np.max(idx) < self._capacity assert 0 <= np.min(idx) return self._value[self._capacity + idx] class SumSegmentTree(SegmentTree): def __init__(self, capacity): super(SumSegmentTree, self).__init__( capacity=capacity, operation=np.add, neutral_element=0.0 ) self._value = np.array(self._value) def sum(self, start=0, end=None): """ Returns arr[start] + ... + arr[end] :param start: (int) start position of the reduction (must be >= 0) :param end: (int) end position of the reduction (must be < len(arr), can be None for len(arr) - 1) :return: (Any) reduction of SumSegmentTree """ return super(SumSegmentTree, self).reduce(start, end) def find_prefixsum_idx(self, prefixsum): """ Find the highest index `i` in the array such that sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum for each entry in prefixsum if array values are probabilities, this function allows to sample indexes according to the discrete probability efficiently. :param prefixsum: (np.ndarray) float upper bounds on the sum of array prefix :return: (np.ndarray) highest indexes satisfying the prefixsum constraint """ if isinstance(prefixsum, float): prefixsum = np.array([prefixsum]) assert 0 <= np.min(prefixsum) assert np.max(prefixsum) <= self.sum() + 1e-5 assert isinstance(prefixsum[0], float) idx = np.ones(len(prefixsum), dtype=int) cont = np.ones(len(prefixsum), dtype=bool) while np.any(cont): # while not all nodes are leafs idx[cont] = 2 * idx[cont] prefixsum_new = np.where( self._value[idx] <= prefixsum, prefixsum - self._value[idx], prefixsum) # prepare update of prefixsum for all right children idx = np.where(np.logical_or( self._value[idx] > prefixsum, np.logical_not(cont)), idx, idx + 1) # Select child node for non-leaf nodes prefixsum = prefixsum_new # update prefixsum cont = idx < self._capacity # collect leafs return idx - self._capacity class MinSegmentTree(SegmentTree): def __init__(self, capacity): super(MinSegmentTree, self).__init__( capacity=capacity, operation=np.minimum, neutral_element=float('inf') ) self._value = np.array(self._value) def min(self, start=0, end=None): """ Returns min(arr[start], ..., arr[end]) :param start: (int) start position of the reduction (must be >= 0) :param end: (int) end position of the reduction (must be < len(arr), can be None for len(arr) - 1) :return: (Any) reduction of MinSegmentTree """ return super(MinSegmentTree, self).reduce(start, end)

py

TiKick

TiKick-main/tmarl/utils/gpu_mem_track.py

import gc import datetime import inspect import torch import numpy as np dtype_memory_size_dict = { torch.float64: 64/8, torch.double: 64/8, torch.float32: 32/8, torch.float: 32/8, torch.float16: 16/8, torch.half: 16/8, torch.int64: 64/8, torch.long: 64/8, torch.int32: 32/8, torch.int: 32/8, torch.int16: 16/8, torch.short: 16/6, torch.uint8: 8/8, torch.int8: 8/8, } # compatibility of torch1.0 if getattr(torch, "bfloat16", None) is not None: dtype_memory_size_dict[torch.bfloat16] = 16/8 if getattr(torch, "bool", None) is not None: dtype_memory_size_dict[torch.bool] = 8/8 # pytorch use 1 byte for a bool, see https://github.com/pytorch/pytorch/issues/41571 def get_mem_space(x): try: ret = dtype_memory_size_dict[x] except KeyError: print(f"dtype {x} is not supported!") return ret class MemTracker(object): """ Class used to track pytorch memory usage Arguments: detail(bool, default True): whether the function shows the detail gpu memory usage path(str): where to save log file verbose(bool, default False): whether show the trivial exception device(int): GPU number, default is 0 """ def __init__(self, detail=True, path='', verbose=False, device=0): self.print_detail = detail self.last_tensor_sizes = set() self.gpu_profile_fn = path + f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_track.txt' self.verbose = verbose self.begin = True self.device = device def get_tensors(self): for obj in gc.get_objects(): try: if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): tensor = obj else: continue if tensor.is_cuda: yield tensor except Exception as e: if self.verbose: print('A trivial exception occured: {}'.format(e)) def get_tensor_usage(self): sizes = [np.prod(np.array(tensor.size())) * get_mem_space(tensor.dtype) for tensor in self.get_tensors()] return np.sum(sizes) / 1024**2 def get_allocate_usage(self): return torch.cuda.memory_allocated() / 1024**2 def clear_cache(self): gc.collect() torch.cuda.empty_cache() def print_all_gpu_tensor(self, file=None): for x in self.get_tensors(): print(x.size(), x.dtype, np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, file=file) def track(self): """ Track the GPU memory usage """ frameinfo = inspect.stack()[1] where_str = frameinfo.filename + ' line ' + str(frameinfo.lineno) + ': ' + frameinfo.function with open(self.gpu_profile_fn, 'a+') as f: if self.begin: f.write(f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |" f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb" f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n") self.begin = False if self.print_detail is True: ts_list = [(tensor.size(), tensor.dtype) for tensor in self.get_tensors()] new_tensor_sizes = {(type(x), tuple(x.size()), ts_list.count((x.size(), x.dtype)), np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, x.dtype) for x in self.get_tensors()} for t, s, n, m, data_type in new_tensor_sizes - self.last_tensor_sizes: f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n') for t, s, n, m, data_type in self.last_tensor_sizes - new_tensor_sizes: f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n') self.last_tensor_sizes = new_tensor_sizes f.write(f"\nAt {where_str:<50}" f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb" f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n")

py

TiKick

TiKick-main/tmarl/utils/modelsize_estimate.py

import torch.nn as nn import numpy as np def modelsize(model, input, type_size=4): para = sum([np.prod(list(p.size())) for p in model.parameters()]) # print('Model {} : Number of params: {}'.format(model._get_name(), para)) print('Model {} : params: {:4f}M'.format(model._get_name(), para * type_size / 1000 / 1000)) input_ = input.clone() input_.requires_grad_(requires_grad=False) mods = list(model.modules()) out_sizes = [] for i in range(1, len(mods)): m = mods[i] if isinstance(m, nn.ReLU): if m.inplace: continue out = m(input_) out_sizes.append(np.array(out.size())) input_ = out total_nums = 0 for i in range(len(out_sizes)): s = out_sizes[i] nums = np.prod(np.array(s)) total_nums += nums # print('Model {} : Number of intermedite variables without backward: {}'.format(model._get_name(), total_nums)) # print('Model {} : Number of intermedite variables with backward: {}'.format(model._get_name(), total_nums*2)) print('Model {} : intermedite variables: {:3f} M (without backward)' .format(model._get_name(), total_nums * type_size / 1000 / 1000)) print('Model {} : intermedite variables: {:3f} M (with backward)' .format(model._get_name(), total_nums * type_size*2 / 1000 / 1000))

py

TiKick

TiKick-main/scripts/football/replay2video.py

"""Script allowing to replay a given trace file. Example usage: python replay.py --trace_file=/tmp/dumps/shutdown_20190521-165136974075.dump """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tmarl.envs.football.env import script_helpers from absl import app from absl import flags FLAGS = flags.FLAGS flags.DEFINE_string('replay_file', None, 'replay file path') flags.DEFINE_string('video_save_dir', '../../results/videos', 'video save dir') flags.DEFINE_integer('fps', 10, 'How many frames per second to render') flags.mark_flag_as_required('replay_file') def main(_): script_helpers.ScriptHelpers().replay(FLAGS.replay_file, FLAGS.fps,directory=FLAGS.video_save_dir) if __name__ == '__main__': app.run(main)

py

criterion.rs

criterion.rs-master/benches/benchmarks/external_process.py

import time import sys def fibonacci(n): if n == 0 or n == 1: return 1 return fibonacci(n - 1) + fibonacci(n - 2) MILLIS = 1000 MICROS = MILLIS * 1000 NANOS = MICROS * 1000 def benchmark(): depth = int(sys.argv[1]) for line in sys.stdin: iters = int(line.strip()) # Setup start = time.perf_counter() for x in range(iters): fibonacci(depth) end = time.perf_counter() # Teardown delta = end - start nanos = int(delta * NANOS) print("%d" % nanos) sys.stdout.flush() benchmark()

py

RobDanns

RobDanns-main/deep_learning/yaml_gen.py

"""Generate yaml files for experiment configurations.""" import yaml # import math import os import re import argparse import numpy as np import shutil def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser() parser.add_argument( '--task', dest='task', help='Generate configs for the given tasks: e.g., mlp_cifar, cnn_cifar, cnn_imagenet, resnet18_tinyimagenet, resenet18_imagenet', default='mlp_cifar10', type=str ) return parser.parse_args() def makedirs_rm_exist(dir): if os.path.isdir(dir): shutil.rmtree(dir) os.makedirs(dir, exist_ok=True) def purge(dir, pattern): for f in os.listdir(dir): if re.search(pattern, f): os.remove(os.path.join(dir, f)) def gen(dir_in, dir_out, fname_base, vars_label, vars_alias, vars_value): '''Generate yaml files''' with open(dir_in + fname_base + '.yaml') as f: data_base = yaml.unsafe_load(f) for vars in vars_value: data = data_base.copy() fname_new = fname_base for id, var in enumerate(vars): if vars_label[id][0] in data: # if key1 exist data[vars_label[id][0]][vars_label[id][1]] = var else: data[vars_label[id][0]] = {vars_label[id][1]: var} if vars_label[id][1] == 'TRANS_FUN': var = var.split('_')[0] fname_new += '_{}{}'.format(vars_alias[id], var) with open(dir_out + fname_new + '.yaml', "w") as f: yaml.dump(data, f, default_flow_style=False) def gen_single(dir_in, dir_out, fname_base, vars_label, vars_alias, vars, comment='best'): '''Generate yaml files for a single experiment''' with open(dir_in + fname_base + '.yaml') as f: data_base = yaml.unsafe_load(f) data = data_base.copy() fname_new = '{}_{}'.format(fname_base, comment) for id, var in enumerate(vars): if vars_label[id][0] in data: # if key1 exist data[vars_label[id][0]][vars_label[id][1]] = var else: data[vars_label[id][0]] = {vars_label[id][1]: var} with open(dir_out + fname_new + '.yaml', "w") as f: yaml.dump(data, f, default_flow_style=False) def grid2list(grid): '''grid search to list''' list_in = [[i] for i in grid[0]] grid.pop(0) for grid_temp in grid: list_out = [] for val in grid_temp: for list_temp in list_in: list_out.append(list_temp + [val]) list_in = list_out return list_in args = parse_args() # Format for all experiments # Note: many arguments are deprecated, they are kept to be consistent with existing experimental results vars_value = [] vars_label = [['RESNET', 'TRANS_FUN'], ['RGRAPH', 'TALK_MODE'], ['RGRAPH', 'GROUP_NUM'], ['RGRAPH', 'MESSAGE_TYPE'], ['RGRAPH', 'SPARSITY'], ['RGRAPH', 'P'], ['RGRAPH', 'AGG_FUNC'], ['RGRAPH', 'SEED_GRAPH'], ['RGRAPH', 'SEED_TRAIN_START'], ['RGRAPH', 'SEED_TRAIN_END'], ['RGRAPH', 'KEEP_GRAPH'], ['RGRAPH', 'ADD_1x1'], ['RGRAPH', 'UPPER'], ['TRAIN', 'AUTO_MATCH'], ['OPTIM', 'MAX_EPOCH'], ['TRAIN', 'CHECKPOINT_PERIOD']] vars_alias = ['trans', 'talkmode', 'num', 'message', 'sparsity', 'p', 'agg', 'graphseed', 'starttrainseed', 'endtrainseed', 'keep', 'add1x1', 'upper', 'match', 'epoch', 'chkpt' ] ## Note: (1) how many relational graphs used to run: graphs_n64_54, graphs_n64_441, graphs_n64_3854 ## (2): "best_id" is to be discovered based on experimental results. Given best_id is for graph2nn experiments ## (3): Each ImageNet experiment provides with 1 seed. One can change SEED_TRAIN_START and SEED_TRAIN_END ## to get results for multiple seeds # usage: python yaml_gen.py --task mlp_cifar10 if args.task == 'mlp_cifar10': # best_id = 3552 # best_id is for graph2nn experiments. fname_bases = ['mlp_bs128_1gpu_layer3'] # graphs = np.load('analysis/graphs_n64_53.npy') # To load the .npy file np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['talklinear_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 6, True, 0, True, True, 200, 200]] vars_value += [['linear_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 6, True, 0, True, True, 200, 200]] # usage : python yaml_gen.py --task cnn_cifar10 if args.task == 'cnn_cifar10': # best_id = 3552 # best_id is for graph2nn experiments. fname_bases = ['cnn6_bs1024_8gpu_64d'] # graphs = np.load('analysis/graphs_n64_53.npy') # To load the .npy file np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['convtalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 6, True, 0, True, True, 100, 100]] vars_value += [['convbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 6, True, 0, True, True, 100, 100]] # uage python yaml_gen.py --task cnn_cifar100 elif args.task == 'cnn_cifar100': # best_id = 3552 # best_id is for graph2nn experiments. fname_bases = ['cnn6_bs640_1gpu_64d'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['convtalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 6, True, 0, True, True, 100, 100]] vars_value += [['convbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 6, True, 0, True, True, 100, 100]] # usage: python yaml_gen.py --task resnet18_tinyimagenet elif args.task == 'resnet18_tinyimagenet': fname_bases = ['R-18_tiny_bs256_1gpu'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupbasictalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 75, 25]] vars_value += [['channelbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 75, 25]] elif args.task == 'cnn_imagenet': # best_id = 27 # best_id is for graph2nn experiments. fname_bases = ['cnn6_bs32_1gpu_64d', 'cnn6_bs256_8gpu_64d'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set to true graphs = np.load('analysis/graphs_n64_53.npy') # restore np.load for future normal usage np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['convtalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 100]] vars_value += [['convbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 100]] # usage : python yaml_gen.py --task resnet18_imagenet elif args.task == 'resnet18_imagenet': # best_id = 37 # best_id is for graph2nn experiments. fname_bases = ['R-18_bs450_1gpu'] # graphs = np.load('analysis/graphs_n64_53.npy') np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) graphs = np.load('analysis/graphs_n64_53.npy') np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupbasictalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 75, 25]] vars_value += [['channelbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 75, 25]] # usage: python yaml_gen.py --task resnet34_imagenet elif args.task == 'resnet34_imagenet': # best_id = 37 # best_id is for graph2nn experiments. fname_bases = ['R-34_bs32_1gpu', 'R-34_bs256_8gpu'] # graphs = np.load('analysis/graphs_n64_52.npy') np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) graphs = np.load('analysis/graphs_n64_53.npy') np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupbasictalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['channelbasic_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] elif args.task == 'resnet34sep_imagenet': # best_id = 36 # best_id is for graph2nn experiments. fname_bases = ['R-34_bs32_1gpu', 'R-34_bs256_8gpu'] graphs = np.load('analysis/graphs_n64_53.npy') for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['groupseptalk_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['channelsep_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] elif args.task == 'resnet50_imagenet': # best_id = 22 # best_id is for graph2nn experiments. fname_bases = ['R-50_bs32_1gpu', 'R-50_bs256_8gpu'] graphs = np.load('analysis/graphs_n64_53.npy') for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['talkbottleneck_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['bottleneck_transform', 'dense', 64, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] # uage : python yaml_gen.py --task efficient_imagenet elif args.task == 'efficient_imagenet': # best_id = 42 # best_id is for graph2nn experiments. fname_bases = ['EN-B0_bs64_1gpu_nms', 'EN-B0_bs512_8gpu_nms'] # graphs = np.load('analysis/graphs_n64_53.npy')) np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) graphs = np.load('analysis/graphs_n64_53.npy') np.load = np_load_old for graph in graphs: sparsity = float(round(graph[1], 6)) randomness = float(round(graph[2], 6)) graphseed = int(graph[3]) vars_value += [['mbtalkconv_transform', 'dense', int(graph[0]), 'ws', sparsity, randomness, 'sum', graphseed, 1, 2, True, 0, True, True, 100, 25]] vars_value += [['mbconv_transform', 'dense', 16, 'ws', 1.0, 0.0, 'sum', 1, 1, 2, True, 0, True, True, 100, 25]] # elif args.task == 'mlp_cifar10_bio': # fname_bases = ['mlp_bs128_1gpu_layer3'] # for graph_type in ['mcwholeraw']: # vars_value += [['talklinear_transform', 'dense', 71, # graph_type, 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] # for graph_type in ['mcvisualraw']: # vars_value += [['talklinear_transform', 'dense', 30, # graph_type, 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] # for graph_type in ['catraw']: # vars_value += [['talklinear_transform', 'dense', 52, # graph_type, 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] # vars_value += [['linear_transform', 'dense', 64, # 'ws', 1.0, 0.0, 'sum', # 1, 1, 6, True, # 0, True, True, 200]] if args.task == 'cifar0': dir_name = 'cifar10' elif 'cifar100' in args.task: dir_name = 'cifar100' elif 'tinyimagenet' in args.task: dir_name = 'tinyimagenet200' else: dir_name = 'imagenet' dir_in = 'configs/baselines/{}/'.format(dir_name) dir_out = 'configs/baselines/{}/{}/'.format(dir_name, args.task) dir_out_all = 'configs/baselines/{}/{}/all/'.format(dir_name, args.task) dir_out_best = 'configs/baselines/{}/{}/best/'.format(dir_name, args.task) # makedirs_rm_exist(dir_out) # makedirs_rm_exist(dir_out_all) # makedirs_rm_exist(dir_out_best) # print(vars_value) for fname_base in fname_bases: if 'bio' not in args.task: gen(dir_in, dir_out_all, fname_base, vars_label, vars_alias, vars_value) # gen_single(dir_in, dir_out_best, fname_base, vars_label, vars_alias, vars_value[best_id], comment='best') gen_single(dir_in, dir_out_best, fname_base, vars_label, vars_alias, vars_value[-1], comment='baseline') else: gen(dir_in, dir_out_best, fname_base, vars_label, vars_alias, vars_value)

py

RobDanns

RobDanns-main/deep_learning/tools/corruptions-inference-tinyimagenet.py

"""Train a classification model.""" from __future__ import print_function import argparse import numpy as np import os import sys import torch import multiprocessing as mp import math import pdb import torch.utils.data import torchvision.datasets as datasets import torchvision.transforms as transforms from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter from PIL import Image import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.paths as dp import time from datetime import datetime from tensorboardX import SummaryWriter from torchvision.utils import save_image from skimage.util import random_noise print("Let's use GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() # TEST(VAL) DATA_LOADER FOR TINY_IMAGENET200 def parseClasses(file): classes = [] filenames = [] with open(file) as f: lines = f.readlines() lines = [x.strip() for x in lines] for x in range(0, len(lines)): tokens = lines[x].split() classes.append(tokens[1]) filenames.append(tokens[0]) return filenames, classes def load_allimages(dir): images = [] if not os.path.isdir(dir): sys.exit(-1) for root, _, fnames in sorted(os.walk(dir)): for fname in sorted(fnames): #if datasets.folder.is_image_file(fname): if datasets.folder.has_file_allowed_extension(fname,['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']): path = os.path.join(root, fname) item = path images.append(item) return images class TinyImageNet(torch.utils.data.Dataset): """ TinyImageNet200 validation dataloader.""" def __init__(self, img_path, gt_path, class_to_idx=None, transform=None): self.img_path = img_path self.transform = transform self.gt_path = gt_path self.class_to_idx = class_to_idx self.classidx = [] self.imgs, self.classnames = parseClasses(gt_path) for classname in self.classnames: self.classidx.append(self.class_to_idx[classname]) def __getitem__(self, index): """inputs: Index, retrns: tuple(im, label)""" img = None with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f: img = Image.open(f) img = img.convert('RGB') if self.transform is not None: img = self.transform(img) label = self.classidx[index] return img, label def __len__(self): return len(self.imgs) def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) eval_stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': eval_stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) # return eval_stats def save_noisy_image(img, name): if img.size(2) == 32: img = img.view(img.size(0), 3, 32, 32) save_image(img, name) if img.size(2) == 64: img = img.view(img.size(0), 3, 64, 64) save_image(img, name) else: img = img.view(img.size(0), 3, 224, 224) save_image(img, name) ## Functions to save noisy images. # def gaussian_noise(test_loader): # print("Adding gaussian_noise") # for data in test_loader: # img, _ = data[0], data[1] # gaussian_img_05 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.05, clip=True)) # gaussian_img_2 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.2, clip=True)) # gaussian_img_4 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.4, clip=True)) # gaussian_img_6 = torch.tensor(random_noise(img, mode='gaussian', mean=0, var=0.6, clip=True)) # save_noisy_image(gaussian_img_05, r"noisy-images/gaussian_05.png") # save_noisy_image(gaussian_img_2, r"noisy-images/gaussian_2.png") # save_noisy_image(gaussian_img_4, r"noisy-images/gaussian_4.png") # save_noisy_image(gaussian_img_6, r"noisy-images/gaussian_6.png") # break # def salt_pepper_noise(test_loader): # print("Adding salt_pepper_noise") # for data in test_loader: # img, _ = data[0], data[1] # s_vs_p_5 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.5, clip=True)) # s_vs_p_6 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.6, clip=True)) # s_vs_p_7 = torch.tensor(random_noise(img, mode='s&p', salt_vs_pepper=0.7, clip=True)) # save_noisy_image(s_vs_p_5, r"noisy-images/s&p_5.png") # break # def speckle_noise(test_loader): # print("Adding speckle_noise") # for data in test_loader: # img, _ = data[0], data[1] # speckle_img_05 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.05, clip=True)) # speckle_img_2 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.2, clip=True)) # speckle_img_4 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.4, clip=True)) # speckle_img_6 = torch.tensor(random_noise(img, mode='speckle', mean=0, var=0.6, clip=True)) # save_noisy_image(speckle_img_05, r"noisy-images/speckle_05.png") # save_noisy_image(speckle_img_2, r"noisy-images/speckle_2.png") # save_noisy_image(speckle_img_4, r"noisy-images/speckle_4.png") # save_noisy_image(speckle_img_6, r"noisy-images/speckle_6.png") # break def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders data_path = dp.get_data_path(cfg.TRAIN.DATASET) # Retrieve the data path for the dataset traindir = os.path.join(data_path, cfg.TRAIN.SPLIT) valdir = os.path.join(data_path, cfg.TEST.SPLIT, 'images') valgtfile = os.path.join(data_path, cfg.TEST.SPLIT, 'val_annotations.txt') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # create training dataset and loader train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=True) # create validation dataset test_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor(), normalize])) # create validation loader test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # Create meters test_meter = TestMeter(len(test_loader)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) noise_mode = ['gaussian', 'speckle', 's&p'] noise_std = [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6] # change the variance values as desired. model.eval() accuracies_gaussian = [] accuracies_saltpepper = [] accuracies_speckle = [] for mode in noise_mode: for level in noise_std: print("Adding noise={} at level={} to images".format(mode, level)) ctr = 0 correct = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader): if not 's&p' in mode: noisy_img = torch.tensor(random_noise(inputs, mode=mode, mean=0, var=level, clip=True)) else: noisy_img = torch.tensor(random_noise(inputs, mode=mode, salt_vs_pepper=0.5, clip=True)) noisy_img, labels = noisy_img.cuda(), labels.cuda(non_blocking=True) outputs = model(noisy_img.float()) _, predicted = torch.max(outputs.data, 1) ctr += 1 total += labels.size(0) correct += (predicted == labels).sum() if total > X: # replace X with the number of images to be generated for adversarial attacks. break acc = 100 * float(correct) / total print("acc =", round(acc, 2), "correct =", float(correct), "total =", total) if 'gaussian' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_gaussian.append(round(acc, 2)) print("Guassian Accuracies after append :", accuracies_gaussian) elif 'speckle' in mode: print('Robust Accuracy = {:.3f} with level = {:.2f}'.format(acc, level)) accuracies_speckle.append(round(acc, 2)) print("Speckle Accuracies after append :", accuracies_speckle) elif 's&p' in mode: print('Robust Accuracy = {:.3f} for S&P noise'.format(acc)) accuracies_saltpepper.append(round(acc, 2)) print("Salt&Pepper Accuracies after append :", accuracies_saltpepper) break else: print("noise mode not supported") # gaussian_noise(test_loader) # salt_pepper_noise(test_loader) # speckle_noise(test_loader) # Change the number of variable as desired number of outputs. gaus_001, gaus_01, gaus_05, gaus_1, gaus_2, gaus_3, gaus_4, gaus_5, gaus_6 = (items for items in accuracies_gaussian) speck_001, speck_01, speck_05, speck_1, speck_2, speck_3, speck_4, speck_5, speck_6 = (items for items in accuracies_speckle) saltpepper = accuracies_saltpepper[0] # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_gaussian = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(gaus_001), str(gaus_01), str(gaus_05), str(gaus_1), str(gaus_2), str(gaus_3), str(gaus_4), str(gaus_5), str(gaus_6)]) result_speck = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(speck_001), str(speck_01), str(speck_05), str(speck_1), str(speck_2), str(speck_3), str(speck_4), str(speck_5), str(speck_6)]) result_sp = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(saltpepper)]) with open("{}/gaus_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Gaussian:{} ".format(accuracies_gaussian)) text_file.write(result_gaussian + '\n') with open("{}/saltpepper_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Salt & Pepper:{} ".format(accuracies_saltpepper)) text_file.write(result_sp + '\n') with open("{}/speckle_noise_stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies Speckle:{} ".format(accuracies_speckle)) text_file.write(result_speck + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Inference seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/train_resnet18_on_tinyimagenet200.py

"""Train a classification model.""" from __future__ import print_function import argparse import numpy as np import os import sys import torch import multiprocessing as mp import math import pdb import torch.utils.data import torchvision.datasets as datasets import torchvision.transforms as transforms from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter from PIL import Image import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.paths as dp import time from datetime import datetime from tensorboardX import SummaryWriter logger = lu.get_logger(__name__) print("Let's use GPU :", torch.cuda.current_device()) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() # TEST/VAL DATA_LOADER FOR TINY_IMAGENET200 def parseClasses(file): classes = [] filenames = [] with open(file) as f: lines = f.readlines() lines = [x.strip() for x in lines] for x in range(0, len(lines)): tokens = lines[x].split() classes.append(tokens[1]) filenames.append(tokens[0]) return filenames, classes def load_allimages(dir): images = [] if not os.path.isdir(dir): sys.exit(-1) for root, _, fnames in sorted(os.walk(dir)): for fname in sorted(fnames): #if datasets.folder.is_image_file(fname): if datasets.folder.has_file_allowed_extension(fname,['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']): path = os.path.join(root, fname) item = path images.append(item) return images class TinyImageNet(torch.utils.data.Dataset): """ TinyImageNet200 validation dataloader.""" def __init__(self, img_path, gt_path, class_to_idx=None, transform=None): self.img_path = img_path self.transform = transform self.gt_path = gt_path self.class_to_idx = class_to_idx self.classidx = [] self.imgs, self.classnames = parseClasses(gt_path) # logger.info('Number of images: {}'.format(len(self.imgs))) # logger.info('Number of classes: {}'.format(len(self.classnames))) for classname in self.classnames: self.classidx.append(self.class_to_idx[classname]) def __getitem__(self, index): """inputs: Index, retrns: tuple(im, label)""" img = None with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f: img = Image.open(f) img = img.convert('RGB') if self.transform is not None: img = self.transform(img) label = self.classidx[index] return img, label def __len__(self): return len(self.imgs) def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops def train_epoch( train_loader, model, loss_fun, optimizer, train_meter, cur_epoch, writer_train=None, params=0, flops=0, is_master=False): """Performs one epoch of training.""" # Shuffle the data loader.shuffle(train_loader, cur_epoch) # Update the learning rate lr = optim.get_epoch_lr(cur_epoch) optim.set_lr(optimizer, lr) # Enable training mode model.train() train_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(train_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Perform the forward pass preds = model(inputs) # Compute the loss loss = loss_fun(preds, labels) # Perform the backward pass optimizer.zero_grad() loss.backward() # Update the parameters optimizer.step() # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the stats across the GPUs if cfg.NUM_GPUS > 1: loss, top1_err, top5_err = du.scaled_all_reduce( [loss, top1_err, top5_err] ) # Copy the stats from GPU to CPU (sync point) loss, top1_err, top5_err = loss.item(), top1_err.item(), top5_err.item() train_meter.iter_toc() # Update and log stats train_meter.update_stats( top1_err, top5_err, loss, lr, inputs.size(0) * cfg.NUM_GPUS ) train_meter.log_iter_stats(cur_epoch, cur_iter) train_meter.iter_tic() # Log epoch stats train_meter.log_epoch_stats(cur_epoch, writer_train, params, flops, is_master=is_master) trg_stats = train_meter.get_epoch_stats(cur_epoch) train_meter.reset() return trg_stats @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) eval_stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': eval_stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) return eval_stats def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(): last_checkpoint = cu.get_checkpoint_last() checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 # Create data loaders # Retrieve the data path for the dataset data_path = dp.get_data_path(cfg.TRAIN.DATASET) traindir = os.path.join(data_path, cfg.TRAIN.SPLIT) valdir = os.path.join(data_path, cfg.TEST.SPLIT, 'images') valgtfile = os.path.join(data_path, cfg.TEST.SPLIT, 'val_annotations.txt') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # create training dataset and loader train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=True) # create validation dataset test_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor(), normalize])) # create validation loader test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # Create meters train_meter = TrainMeter(len(train_loader)) test_meter = TestMeter(len(test_loader)) # Create meters for fgsm test_meter_fgsm = TestMeter(len(test_loader_adv)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) # do eval at initialization initial_eval_stats = eval_epoch(test_loader, model, test_meter, -1, writer_eval, params, flops, is_master=is_master) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 last_epoch_eval_stats = eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) else: for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH): print('Epoch {} Started'.format(cur_epoch)) # Train for one epoch trg_stats = train_epoch( train_loader, model, loss_fun, optimizer, train_meter, cur_epoch, writer_train, is_master=is_master ) # Compute precise BN stats if cfg.BN.USE_PRECISE_STATS: nu.compute_precise_bn_stats(model, train_loader) # Save a checkpoint if cu.is_checkpoint_epoch(cur_epoch): checkpoint_file = cu.save_checkpoint(model, optimizer, cur_epoch) logger.info('Wrote checkpoint to: {}'.format(checkpoint_file)) # Evaluate the model if is_eval_epoch(cur_epoch): eval_stats = eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Train the model train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): if cfg.NUM_GPUS > 1: mpu.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=single_proc_train) else: single_proc_train() else: print('Seed {} exists, skip!'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/adversarial-inference-tinyimagenet200.py

"""Train a classification model.""" from __future__ import print_function import argparse import numpy as np import os import sys import torch import multiprocessing as mp import math import pdb import torch.utils.data import torchvision.datasets as datasets import torchvision.transforms as transforms from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter from pycls.utils.meters import TrainMeter from PIL import Image import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.paths as dp import time from datetime import datetime from tensorboardX import SummaryWriter print("Let's use GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() # TEST/VAL DATA_LOADER FOR TINY_IMAGENET200 def parseClasses(file): classes = [] filenames = [] with open(file) as f: lines = f.readlines() lines = [x.strip() for x in lines] for x in range(0, len(lines)): tokens = lines[x].split() classes.append(tokens[1]) filenames.append(tokens[0]) return filenames, classes def load_allimages(dir): images = [] if not os.path.isdir(dir): sys.exit(-1) for root, _, fnames in sorted(os.walk(dir)): for fname in sorted(fnames): # if datasets.folder.is_image_file(fname): if datasets.folder.has_file_allowed_extension(fname,['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']): path = os.path.join(root, fname) item = path images.append(item) return images class TinyImageNet(torch.utils.data.Dataset): """ TinyImageNet200 validation dataloader.""" def __init__(self, img_path, gt_path, class_to_idx=None, transform=None): self.img_path = img_path self.transform = transform self.gt_path = gt_path self.class_to_idx = class_to_idx self.classidx = [] self.imgs, self.classnames = parseClasses(gt_path) for classname in self.classnames: self.classidx.append(self.class_to_idx[classname]) def __getitem__(self, index): """inputs: Index, retrns: tuple(im, label)""" img = None with open(os.path.join(self.img_path, self.imgs[index]), 'rb') as f: img = Image.open(f) img = img.convert('RGB') if self.transform is not None: img = self.transform(img) label = self.classidx[index] return img, label def __len__(self): return len(self.imgs) def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) eval_stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': eval_stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) # return eval_stats class Normalize(torch.nn.Module): def __init__(self, mean, std): super(Normalize, self).__init__() self.register_buffer('mean', torch.Tensor(mean)) self.register_buffer('std', torch.Tensor(std)) def forward(self, input): # Broadcasting mean = self.mean.reshape(1,3,1,1) std = self.std.reshape(1,3,1,1) norm_img = (input - mean) / std return norm_img # Helper class for printing model layers class PrintLayer(torch.nn.Module): def __init__(self): super(PrintLayer, self).__init__() def forward(self, x): # Do your print / debug stuff here print(x) return x def train_model(writer_train=None, writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) # for name, param in model.named_parameters(): # print(name, param.shape) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders data_path = dp.get_data_path(cfg.TRAIN.DATASET) # Retrieve the data path for the dataset traindir = os.path.join(data_path, cfg.TRAIN.SPLIT) valdir = os.path.join(data_path, cfg.TEST.SPLIT, 'images') valgtfile = os.path.join(data_path, cfg.TEST.SPLIT, 'val_annotations.txt') # normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # create training dataset and loader train_loader = torch.utils.data.DataLoader( datasets.ImageFolder(traindir, transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])), batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS), shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=True) # create validation dataset test_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor(), normalize])) # create validation loader test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=int(cfg.TEST.BATCH_SIZE / cfg.NUM_GPUS), shuffle=False, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # create adversarial dataset adv_dataset = TinyImageNet( valdir, valgtfile, class_to_idx=train_loader.dataset.class_to_idx.copy(), transform=transforms.Compose([ transforms.Resize(224), transforms.ToTensor()])) # create adversarial loader test_loader_adv = torch.utils.data.DataLoader( adv_dataset, batch_size=1, shuffle=True, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False) # Create meters test_meter = TestMeter(len(test_loader)) test_meter_adv = TestMeter(len(test_loader_adv)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) # when epsilon=0 --> PGD, epsilon=1 --> CW, otherwise FGSM-->replace eps1, eps2, ... with required epsilon of attack versions epsilons = [0, eps1, eps2, ... epsN, 1] # Per-channel mean and SD values in BGR order for TinyImageNet dataset tinyimagenet_MEAN = [0.485, 0.456, 0.406] tinyimagenet_SD = [0.229, 0.224, 0.225] accuracies = [] # add normalization layer to the model norm_layer = Normalize(mean=tinyimagenet_MEAN, std=tinyimagenet_SD) net = torch.nn.Sequential(norm_layer, model).cuda() net = net.eval() for epsilon in epsilons: if epsilon == 0: print("Running PGD Attack") atk = torchattacks.PGD(net, eps=1/510, alpha=2/225, steps=7) # for relevant dataset, use parameters from torchattacks official notebook elif epsilon == 1: print("Running CW Attack") atk = torchattacks.CW(net, c=0.1, kappa=0, steps=100, lr=0.01) # choose suitable values for c, kappa, steps, and lr. else: print("Running FGSM Attacks on epsilon :", epsilon) atk = torchattacks.FGSM(net, eps=epsilon) ctr = 0 correct = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader_adv): inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) adv_images = atk(inputs, labels) outputs = net(adv_images) _, predicted = torch.max(outputs.data, 1) ctr += 1 total += 1 correct += (predicted == labels).sum() if ctr > X: # replace X with the number of images to be generated for adversarial attacks. print(ctr, " images done for epsilon:", epsilon) break acc = 100 * float(correct) / total print("acc =", round(acc, 2), "correct =", float(correct), "total =", total) accuracies.append(round(acc, 2)) print('Attack Accuracy = {:.3f} with epsilon = {:.4f}'.format(acc, epsilon)) print("accuracies after apend :", accuracies) # save items inside accuracies list to separate float objects, update the # of variables according to requirement. accPGD, accFGSM1, accFGSM2, accFGSM3, accFGSM4, accFGSM5, accFGSM6, accFGSM7, accCW = (items for items in accuracies) # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_info = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(accPGD), str(accFGSM1), str(accFGSM2), str(accFGSM3), str(accFGSM4), str(accFGSM5), str(accFGSM6), str(accFGSM7), str(accCW)]) with open("{}/stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies {} ".format(accuracies)) text_file.write(result_info + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None ## If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_train, writer_eval, is_master=du.is_master_proc()) if writer_train is not None and writer_eval is not None: writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Inference seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py

RobDanns

RobDanns-main/deep_learning/tools/adversarial-inference.py

"""Train a classification model.""" import argparse import pickle import numpy as np import os import sys import torch import math import torchvision import torchattacks from pycls.config import assert_cfg from pycls.config import cfg from pycls.config import dump_cfg from pycls.datasets import loader from pycls.models import model_builder from pycls.utils.meters import TestMeter import pycls.models.losses as losses import pycls.models.optimizer as optim import pycls.utils.checkpoint as cu import pycls.utils.distributed as du import pycls.utils.logging as lu import pycls.utils.metrics as mu import pycls.utils.multiprocessing as mpu import pycls.utils.net as nu import pycls.datasets.transforms as transforms from datetime import datetime from tensorboardX import SummaryWriter import foolbox as fb import art import art.attacks.evasion as evasion from art.estimators.classification import PyTorchClassifier print("Using GPU :", torch.cuda.current_device()) logger = lu.get_logger(__name__) def parse_args(): """Parses the arguments.""" parser = argparse.ArgumentParser( description='Train a classification model' ) parser.add_argument( '--cfg', dest='cfg_file', help='Config file', required=True, type=str ) parser.add_argument( 'opts', help='See pycls/core/config.py for all options', default=None, nargs=argparse.REMAINDER ) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def is_eval_epoch(cur_epoch): """Determines if the model should be evaluated at the current epoch.""" return ( (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0 or (cur_epoch + 1) == cfg.OPTIM.MAX_EPOCH ) def log_model_info(model, writer_eval=None): """Logs model info""" logger.info('Model:\n{}'.format(model)) params = mu.params_count(model) flops = mu.flops_count(model) logger.info('Params: {:,}'.format(params)) logger.info('Flops: {:,}'.format(flops)) logger.info('Number of node: {:,}'.format(cfg.RGRAPH.GROUP_NUM)) # logger.info('{}, {}'.format(params,flops)) if writer_eval is not None: writer_eval.add_scalar('Params', params, 1) writer_eval.add_scalar('Flops', flops, 1) return params, flops @torch.no_grad() def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False): """Evaluates the model on the test set.""" # Enable eval mode model.eval() test_meter.iter_tic() # val_input_imgs, for cur_iter, (inputs, labels) in enumerate(test_loader): # Transfer the data to the current GPU device inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) # Compute the predictions preds = model(inputs) # Compute the errors top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5]) # Combine the errors across the GPUs if cfg.NUM_GPUS > 1: top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err]) # Copy the errors from GPU to CPU (sync point) top1_err, top5_err = top1_err.item(), top5_err.item() test_meter.iter_toc() # Update and log stats test_meter.update_stats( top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS ) test_meter.log_iter_stats(cur_epoch, cur_iter) test_meter.iter_tic() # Log epoch stats # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops) test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master) stats = test_meter.get_epoch_stats(cur_epoch) test_meter.reset() if cfg.RGRAPH.SAVE_GRAPH: adj_dict = nu.model2adj(model) adj_dict = {**adj_dict, 'top1_err': stats['top1_err']} os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True) np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict) class Normalize(torch.nn.Module): def __init__(self, mean, std): super(Normalize, self).__init__() self.register_buffer('mean', torch.Tensor(mean)) self.register_buffer('std', torch.Tensor(std)) def forward(self, input): # Broadcasting mean = self.mean.reshape(1,3,1,1) std = self.std.reshape(1,3,1,1) norm_img = (input - mean) / std return norm_img # Helper class for printing model layers class PrintLayer(torch.nn.Module): def __init__(self): super(PrintLayer, self).__init__() def forward(self, x): # Do your print / debug stuff here print(x) return x def train_model(writer_eval=None, is_master=False): """Trains the model.""" # Fit flops/params if cfg.TRAIN.AUTO_MATCH and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: mode = 'flops' # flops or params if cfg.TRAIN.DATASET == 'cifar10': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': # ResNet20 stats_baseline = 40813184 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 64: stats_baseline = 48957952 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'cifar100': pre_repeat = 15 if cfg.MODEL.TYPE == 'resnet': if cfg.MODEL.DEPTH == 20: stats_baseline = 40813184 # ResNet20 elif cfg.MODEL.DEPTH == 26: stats_baseline = 56140000 # ResNet26 elif cfg.MODEL.DEPTH == 34: stats_baseline = 71480000 # ResNet34 elif cfg.MODEL.DEPTH == 38: stats_baseline = 86819000 # ResNet38 elif cfg.MODEL.DEPTH == 50: stats_baseline = 130000000 # ResNet50 elif cfg.MODEL.TYPE == 'mlpnet': # 5-layer MLP. cfg.MODEL.LAYERS exclude stem and head layers if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 256: stats_baseline = 985600 elif cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 2364416 elif cfg.RGRAPH.DIM_LIST[0] == 1024: stats_baseline = 6301696 elif cfg.MODEL.TYPE == 'cnn': if cfg.MODEL.LAYERS == 3: if cfg.RGRAPH.DIM_LIST[0] == 512: stats_baseline = 806884352 elif cfg.RGRAPH.DIM_LIST[0] == 16: stats_baseline = 1216672 elif cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 48957952 elif '16d' in cfg.OUT_DIR: stats_baseline = 3392128 elif cfg.TRAIN.DATASET == 'tinyimagenet200': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 elif cfg.TRAIN.DATASET == 'imagenet': pre_repeat = 9 if cfg.MODEL.TYPE == 'resnet': if 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 18: # ResNet18 stats_baseline = 1820000000 elif 'basic' in cfg.RESNET.TRANS_FUN and cfg.MODEL.DEPTH == 34: # ResNet34 stats_baseline = 3663761408 elif 'sep' in cfg.RESNET.TRANS_FUN: # ResNet34-sep stats_baseline = 553614592 elif 'bottleneck' in cfg.RESNET.TRANS_FUN: # ResNet50 stats_baseline = 4089184256 elif cfg.MODEL.TYPE == 'efficientnet': # EfficientNet stats_baseline = 385824092 elif cfg.MODEL.TYPE == 'cnn': # CNN if cfg.MODEL.LAYERS == 6: if '64d' in cfg.OUT_DIR: stats_baseline = 166438912 cfg.defrost() stats = model_builder.build_model_stats(mode) if stats != stats_baseline: # 1st round: set first stage dim for i in range(pre_repeat): scale = round(math.sqrt(stats_baseline / stats), 2) first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first = int(round(first * scale)) cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 step = 1 while True: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first += flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if stats == stats_baseline: break if flag != flag_init: if cfg.RGRAPH.UPPER == False: # make sure the stats is SMALLER than baseline if flag < 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break else: if flag > 0: first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [dim / first for dim in cfg.RGRAPH.DIM_LIST] first -= flag_init * step cfg.RGRAPH.DIM_LIST = [int(round(first * ratio)) for ratio in ratio_list] break # 2nd round: set other stage dim first = cfg.RGRAPH.DIM_LIST[0] ratio_list = [int(round(dim / first)) for dim in cfg.RGRAPH.DIM_LIST] stats = model_builder.build_model_stats(mode) flag_init = 1 if stats < stats_baseline else -1 if 'share' not in cfg.RESNET.TRANS_FUN: for i in range(1, len(cfg.RGRAPH.DIM_LIST)): for j in range(ratio_list[i]): cfg.RGRAPH.DIM_LIST[i] += flag_init stats = model_builder.build_model_stats(mode) flag = 1 if stats < stats_baseline else -1 if flag_init != flag: cfg.RGRAPH.DIM_LIST[i] -= flag_init break stats = model_builder.build_model_stats(mode) print('FINAL', cfg.RGRAPH.GROUP_NUM, cfg.RGRAPH.DIM_LIST, stats, stats_baseline, stats < stats_baseline) # Build the model (before the loaders to ease debugging) model = model_builder.build_model() params, flops = log_model_info(model, writer_eval) if cfg.IS_INFERENCE and cfg.IS_DDP: model = torch.nn.parallel.DataParallel(model) # for name, param in model.named_parameters(): # print(name, param.shape) # Define the loss function loss_fun = losses.get_loss_fun() # Construct the optimizer optimizer = optim.construct_optimizer(model) # Load a checkpoint if applicable start_epoch = 0 if cu.had_checkpoint(): print("Checking for a checkpoint") last_checkpoint = cu.get_checkpoint_last() print("Last Checkpoint : ", last_checkpoint) checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model, optimizer) logger.info('Loaded checkpoint from: {}'.format(last_checkpoint)) if checkpoint_epoch == cfg.OPTIM.MAX_EPOCH: exit() start_epoch = checkpoint_epoch else: start_epoch = checkpoint_epoch + 1 print("Epoch = ", start_epoch) # Create data loaders test_loader = loader.construct_test_loader() test_loader_adv = loader.construct_test_loader_adv() # Create meters test_meter = TestMeter(len(test_loader)) test_meter_adv = TestMeter(len(test_loader_adv)) if cfg.ONLINE_FLOPS: model_dummy = model_builder.build_model() IMAGE_SIZE = 224 n_flops, n_params = mu.measure_model(model_dummy, IMAGE_SIZE, IMAGE_SIZE) logger.info('FLOPs: %.2fM, Params: %.2fM' % (n_flops / 1e6, n_params / 1e6)) del (model_dummy) # Perform the training loop logger.info('Start epoch: {}'.format(start_epoch + 1)) if start_epoch == cfg.OPTIM.MAX_EPOCH: cur_epoch = start_epoch - 1 eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval, params, flops, is_master=is_master) # when epsilon=0, 1 --> PGD, epsilon=2, 3 --> CW, otherwise FGSM-->replace eps1, eps2, ... with required epsilon of attack versions epsilons = [0, 1, eps1, eps2, ... epsN, 2, 3] # Per-channel mean and SD values in BGR order for ImageNet dataset cifar10_MEAN = [0.491, 0.482, 0.4465] cifar10_SD = [0.247, 0.243, 0.262] cifar100_MEAN = [0.507, 0.487, 0.441] cifar100_SD = [0.267, 0.256, 0.276] imagenet_MEAN = [0.406, 0.456, 0.485] imagenet_SD = [0.225, 0.224, 0.229] accuracies = [] # replace the MEAN and SD variable in the following line for the relevant dataset. norm_layer = Normalize(mean=cifar10_MEAN, std=cifar10_SD) net = torch.nn.Sequential(norm_layer, model).cuda() # net = torch.nn.Sequential(norm_layer, PrintLayer(), model).cuda() net = net.eval() print("Adversarial Loader Batch Size =", test_loader_adv.batch_size) for epsilon in epsilons: if epsilon == 0: print("Running PGD Attack") atk_ta = torchattacks.PGD(net, eps=6/255, alpha=2/255, steps=7) # for relevant dataset, use parameters from torchattacks official notebook elif epsilon == 1: print("Running PGD Attack") atk_ta = torchattacks.PGD(net, eps=9/255, alpha=2/255, steps=7) # for relevant dataset, use parameters from torchattacks official notebook elif epsilon == 2: print("Running Torchattacks.CW") atk_ta = torchattacks.CW(net, c=0.15, kappa=0, steps=100, lr=0.01) # replace the values of c and steps according to hyperparameters reported in the paper. elif epsilon == 3: print("Running Torchattacks.CW") atk_ta = torchattacks.CW(net, c=0.25, kappa=0, steps=100, lr=0.01) # replace the values of c and steps according to hyperparameters reported in the paper. # For Foolbox or ART attacks, uncomment the following lines. # print("-> FoolBox.CW") # fmodel = fb.PyTorchModel(net, bounds=(0, 1)) # atk_fb = fb.attacks.L2CarliniWagnerAttack(binary_search_steps=1, initial_const=0.05, # confidence=0, steps=100, stepsize=0.01) # print("-> Adversarial Robustness Toolbox.CW") # classifier = PyTorchClassifier(model=net, clip_values=(0, 1), # loss=loss_fun, # optimizer=optimizer, # input_shape=(3, 32, 32), nb_classes=10) # atk_art = evasion.CarliniL2Method(batch_size=1, classifier=classifier, # binary_search_steps=1, initial_const=0.05, # confidence=0, max_iter=100, # learning_rate=0.01) else: print("Running FGSM Attacks on epsilon :", epsilon) atk_ta = torchattacks.FGSM(net, eps=epsilon) ctr = 0 correct_ta = 0 # correct_fb = 0 # correct_art = 0 total = 0 for cur_iter, (inputs, labels) in enumerate(test_loader_adv): inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True) inputs = inputs.float().div(255) adv_images_ta = atk_ta(inputs, labels) # _, adv_images_fb, _ = atk_fb(fmodel, inputs, labels, epsilons=1) # adv_images_art = torch.tensor(atk_art.generate(inputsnp, labelsnp)).cuda() adv_inputs_ta = adv_images_ta.float() # adv_inputs_fb = adv_images_fb.float() # adv_inputs_art = adv_images_art.float() outputs_ta = net(adv_inputs_ta) # outputs_fb = net(adv_inputs_fb) # outputs_art = net(adv_inputs_art) _, predicted_ta = torch.max(outputs_ta.data, 1) # _, predicted_fb = torch.max(outputs_fb.data, 1) # _, predicted_art = torch.max(outputs_art.data, 1) ctr += 1 total += 1 correct_ta += (predicted_ta == labels).sum() # correct_fb += (predicted_fb == labels).sum() # correct_art += (predicted_art == labels).sum() if ctr > X: # replace X with the number of images to be generated for adversarial attacks. print(ctr, " images done for epsilon:", epsilon) break acc_ta = 100 * float(correct_ta) / total # acc_fb = 100 * float(correct_fb) / total # acc_art = 100 * float(correct_art) / total print("ta acc =", round(acc_ta, 2), ", ta correct =", float(correct_ta), ", total =", total) # print("fb acc =", round(acc_fb, 2), ", fb correct =", float(correct_fb), ", total =", total) # print("art acc =", round(acc_art, 2), ", art correct =", float(correct_art), ", total =", total) accuracies.append(round(acc_ta, 2)) print('Attack Accuracy = {:.3f} with epsilon = {:.2f}'.format(acc_ta, epsilon)) print("accuracies after apend :", accuracies) # save items inside accuracies list to separate float objects, update the # of variables according to requirement. accPGD_6by255, accPGD_9by255, accFGSM1, accFGSM2, accFGSM3, accFGSM4, accFGSM5, accCW_15, accCW_25 = (items for items in accuracies) # load the top1 error and top5 error from the evaluation results f = open("{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH), "r") c_ids = [] for i in f.readlines(): sub_id = list(map(float, i.split(","))) c_ids.append(sub_id[3:5]) topK_errors = [sum(i) / len(c_ids) for i in zip(*c_ids)] top1_error, top5_error = topK_errors[0], topK_errors[1] result_info = ', '.join( [str(cfg.RGRAPH.GROUP_NUM), str(cfg.RGRAPH.P), str(cfg.RGRAPH.SPARSITY), '{:.3f}'.format(top1_error), '{:.3f}'.format(top5_error), str(accPGD_6by255), str(accPGD_9by255), str(accFGSM1), str(accFGSM2), str(accFGSM3), str(accFGSM4), str(accFGSM5), str(accCW_15), str(accCW_25)]) # with open("{}/stats.txt".format(cfg.OUT_DIR), "a") as text_file: print(" Writing Text File with accuracies {} ".format(accuracies)) text_file.write(result_info + '\n') def single_proc_train(): """Performs single process training.""" # Setup logging lu.setup_logging() # Show the config logger.info('Config:\n{}'.format(cfg)) # Setup tensorboard if provided writer_train = None writer_eval = None # If use tensorboard if cfg.TENSORBOARD and du.is_master_proc() and cfg.RGRAPH.SEED_TRAIN == cfg.RGRAPH.SEED_TRAIN_START: comment = '' current_time = datetime.now().strftime('%b%d_%H-%M-%S') logdir_train = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_train') logdir_eval = os.path.join(cfg.OUT_DIR, 'runs', current_time + comment + '_eval') if not os.path.exists(logdir_train): os.makedirs(logdir_train) if not os.path.exists(logdir_eval): os.makedirs(logdir_eval) writer_train = SummaryWriter(logdir_train) writer_eval = SummaryWriter(logdir_eval) # Fix the RNG seeds (see RNG comment in core/config.py for discussion) np.random.seed(cfg.RGRAPH.SEED_TRAIN) torch.manual_seed(cfg.RGRAPH.SEED_TRAIN) # Configure the CUDNN backend torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK # Launch inference + adversarial run train_model(writer_eval, is_master=du.is_master_proc()) if writer_eval is not None: # writer_train.close() writer_eval.close() def check_seed_exists(i): fname = "{}/results_epoch{}.txt".format(cfg.OUT_DIR, cfg.OPTIM.MAX_EPOCH) if os.path.isfile(fname): with open(fname, 'r') as f: lines = f.readlines() if len(lines) > i: return True return False def main(): # Parse cmd line args args = parse_args() # Load config options cfg.merge_from_file(args.cfg_file) cfg.merge_from_list(args.opts) assert_cfg() # cfg.freeze() # Ensure that the output dir exists os.makedirs(cfg.OUT_DIR, exist_ok=True) # Save the config dump_cfg() for i, cfg.RGRAPH.SEED_TRAIN in enumerate(range(cfg.RGRAPH.SEED_TRAIN_START, cfg.RGRAPH.SEED_TRAIN_END)): # check if a seed has been run if not check_seed_exists(i): print("Launching inference for seed {}".format(i)) single_proc_train() else: print('Trained seed {} already exists, stopping inference'.format(cfg.RGRAPH.SEED_TRAIN)) if __name__ == '__main__': main()

py