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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from Model.CLIP.cn_clip.clip import load_from_name
import cn_clip.clip as clip
from PIL import Image
from torch.autograd import Variable
from fur_rl.models.StageOne import Stage1
from fur_rl.models.transformer import ModelAttn
import copy
import PIL
# v3 带softmax的模型,actor
class Retriever_v3(nn.Module):
def __init__(self, sentence_clip_model2, sentence_clip_preprocess2, device):
super(Retriever_v3, self).__init__()
self.sentence_clip_model2 = sentence_clip_model2
self.sentence_clip_preprocess2 = sentence_clip_preprocess2
self.tokenize = clip.tokenize
self.Stage1 = Stage1(hid_dim=512)
self.fc02 = nn.Linear(in_features=512, out_features=1, bias=True)
self.fc03 = nn.Linear(in_features=256, out_features=64, bias=True)
self.fc04 = nn.Linear(in_features=64, out_features=1, bias=True)
self.fc12 = nn.Linear(in_features=512, out_features=1, bias=True)
self.fc13 = nn.Linear(in_features=256, out_features=64, bias=True)
self.fc14 = nn.Linear(in_features=64, out_features=1, bias=True)
# self.fc1 = nn.Linear(in_features=516, out_features=1, bias=True)
self.fc2 = nn.Linear(in_features=512, out_features=512, bias=True)
self.logistic = nn.Sigmoid()
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=1)
self.bn = nn.BatchNorm1d(num_features=1)
self.Attn0 = ModelAttn()
self.Attn1 = ModelAttn()
self.Attn_his = ModelAttn()
self.norm = nn.LayerNorm(normalized_shape=512)
self.norm50 = nn.LayerNorm(normalized_shape=50)
self.Attn_seq = ModelAttn()
self.fc_seq0 = nn.Linear(in_features=512, out_features=1, bias=True)
self.fc_seq1 = nn.Linear(in_features=512, out_features=1, bias=True)
self.hx_his = None
self.device = device
def img_embed(self, img):
img_embed = self.sentence_clip_model2.encode_image(img)
img_embed = img_embed / img_embed.norm(dim=-1, keepdim=True)
return img_embed
def txt_embed(self, txt_token):
txt_embed = self.sentence_clip_model2.encode_text(txt_token)
txt_embed = txt_embed / txt_embed.norm(dim=-1, keepdim=True)
return txt_embed
def get_env_feature(self, img, txt, reward):
hx = self.Stage1.hx
x = self.Stage1(img, txt, reward)
x = x / x.norm(dim=-1, keepdim=True)
return x, hx
def init_hid(self, batch_size=1):
self.hx_his = torch.zeros((batch_size, 1, 512), device=self.device)
return
def detach_hid(self):
self.hx_his = None
return
def get_Q(self, img, txt, action, reward, hx_his):
img = img.unsqueeze(dim=1)
txt = txt.unsqueeze(dim=1)
# print("img:\n", img, img.shape)
# print("txt:\n", txt, txt.shape)
# env_feature = self.Attn0(img, txt, txt)
env_feature = txt
env_feature1 = env_feature / env_feature.norm(dim=-1, keepdim=True)
# print("hx1 and txt:\n", env_feature1[0] @ txt[0].T)
hx_his = hx_his.unsqueeze(dim=1)
# print("hx1 and hx_his\n", env_feature1[0] @ hx_his[0].T)
hx2 = self.Attn_his(env_feature1, hx_his, hx_his)
hx2 = hx2 / hx2.norm(dim=-1, keepdim=True)
# print("hx2 and hx_his:\n", hx2[0] @ hx_his[0].T)
# print("hx2 and hx1:\n", hx2[0] @ env_feature1[0].T)
# transformers:
action = action.unsqueeze(dim=1)
x = self.Attn1(action, hx2, hx2) # 16 1 512
# linear:
p = torch.zeros((action.shape[0], 1), device=self.device)
for i in range(action.shape[0]):
p[i] = hx2[i] @ action[i].T
# p = self.logistic(self.fc02(x))
# p = self.logistic(self.fc03(p))
# p = self.logistic(self.fc04(p))
q = self.logistic(self.fc12(x))
# q = self.logistic(self.fc13(q))
# q = self.logistic(self.fc14(q))
# p = p.squeeze(dim=1)
q = q.squeeze(dim=1)
return (p + 1) / 2, q, hx2
def forward(self, img, txt, actions, hx_his, r):
# action [B, 50, 512]
# img [B, step, 512]
# txt [B, step, 512]
x = self.Attn_seq(q=actions, k=txt, v=txt) # [B, 50, 512]
p = self.softmax(self.fc_seq0(x)) # [B, 50, 1]
q = self.logistic(self.fc_seq1(x)) # [B, 50, 1]
p = torch.clamp(p, min=1e-10, max=1-1e-10)
return p.squeeze(2), q, hx_his
MEMORY_CAPACITY = 100
N_STATES = 4
GAMMA = 0.95
class DQN_v3():
def __init__(self, sentence_clip_model2, sentence_clip_preprocess2, device, MULTI_GPU=False, device_ids=None):
self.actor_net = Retriever_v3(sentence_clip_model2, sentence_clip_preprocess2, device).to(device)
self.num_train = 0
self.device = device
self.MULTI_GPU = MULTI_GPU
self.device_ids = device_ids
self.memory = [] # initialize memory
self.neg_memory = []
self.memory_counter = 0
self.actor_optimizer = torch.optim.Adam(self.actor_net.parameters(), lr=0.00001)
self.loss_func1 = nn.MSELoss()
self.loss_func2 = nn.MSELoss()
self.batch_mem = {}
self.is_store = None
def store_transition(self, g, f, c, hx, a, r, g_, f_, c_, hx_, t, batch_size, net_mem, success_turn, turn):
if turn == 0:
for i in range(batch_size):
self.batch_mem[i] = []
self.is_store = torch.zeros((batch_size), device=self.device)
for i in range(batch_size):
if r[i] <= -2000:
continue
else:
f_seq = torch.zeros((12, 512), device=self.device)
f_seq[0:turn+2] = f[:,i]
f_seq_ = torch.zeros((12, 512), device=self.device)
f_seq_[0:turn+3] = f_[:,i]
g_tmp = copy.deepcopy(g[i])
f_tmp = copy.deepcopy(f_seq) # [len, 512]
c_tmp = copy.deepcopy(c[i])
hx_tmp = copy.deepcopy(hx[i])
a_tmp = copy.deepcopy(a[i])
reward_temp = copy.deepcopy(r[i])
g_tmp_ = copy.deepcopy(g_[i])
f_tmp_ = copy.deepcopy(f_seq_)
c_tmp_ = copy.deepcopy(c_[i])
hx_tmp_ = copy.deepcopy(hx_[i])
t_tmp = copy.deepcopy(t[i])
self.batch_mem[i].append((g_tmp, f_tmp, c_tmp, hx_tmp, a_tmp, reward_temp,
g_tmp_, f_tmp_, c_tmp_, hx_tmp_, t_tmp))
if success_turn[i] > 0 and self.is_store[i] == 0:
# print("len", len(self.memory), len(self.batch_mem[i]))
self.memory.append(self.batch_mem[i])
self.is_store[i] = 1
# self.memory.append((g_tmp, f_tmp, c_tmp, hx_tmp, a_tmp, reward_temp,
# g_tmp_, f_tmp_, c_tmp_, hx_tmp_, t_tmp))
# print("len", len(self.memory))
while len(self.memory) > net_mem:
self.memory.pop(0)
self.memory_counter = len(self.memory)
# print("success", success_turn[i], reward_temp, len(self.memory))
elif turn == 9 and self.is_store[i] == 0:
self.neg_memory.append(self.batch_mem[i])
while len(self.neg_memory) > net_mem+16:
self.neg_memory.pop(0)
# print("neg", success_turn[i], reward_temp, len(self.neg_memory))
def learn(self, batch_size, device=None, i=0):
if len(self.neg_memory) < len(self.memory):
return 0, 0
# target parameter update
actor_loss = 0
critic_loss = 0
batch_mem = self.memory[i]
g, f, c, hx, a, r, g_, f_, c_, hx_, t = zip(*[batch_mem[i] for i in range(len(batch_mem))])
b_g = torch.stack(g)
b_f = torch.stack(f)
b_c = torch.stack(c)
b_hx = torch.stack(hx)
b_a = torch.stack(a).unsqueeze(1)
# b_r = torch.stack(r)
b_r = torch.zeros((len(r), 1), device=self.device)
for i in range(len(r)):
if i == 0:
b_r[len(r) - i - 1] = r[len(r) - i - 1]
else:
b_r[len(r) - i - 1] = 0.5 * b_r[len(r) - i]
b_g_ = torch.stack(g_)
b_f_ = torch.stack(f_)
b_c_ = torch.stack(c_)
b_hx_ = torch.stack(hx_)
b_t = torch.stack(t)
batch_mem = self.neg_memory[i]
g, f, c, hx, a, r, g_, f_, c_, hx_, t = zip(*[batch_mem[i] for i in range(len(batch_mem))])
b_g_neg = torch.stack(g)
b_f_neg = torch.stack(f)
b_c_neg = torch.stack(c)
b_hx_neg = torch.stack(hx)
b_a_neg = torch.stack(a).unsqueeze(1)
# b_r_neg = torch.stack(r)
b_r_neg = torch.zeros((len(r), 1), device=self.device)
for i in range(len(r)):
if i == 0:
b_r_neg[len(r) - i - 1] = r[len(r) - i - 1]
else:
b_r_neg[len(r) - i - 1] = 0.5 * b_r_neg[len(r) - i]
b_g_neg_ = torch.stack(g_)
b_f_neg_ = torch.stack(f_)
b_c_neg_ = torch.stack(c_)
b_hx_neg_ = torch.stack(hx_)
b_t_neg = torch.stack(t)
b_g = torch.cat((b_g, b_g_neg), 0)
b_f = torch.cat((b_f, b_f_neg), 0)
b_c = torch.cat((b_c, b_c_neg), 0)
b_hx = torch.cat((b_hx, b_hx_neg), 0)
b_a = torch.cat((b_a, b_a_neg), 0)
b_r = torch.cat((b_r, b_r_neg), 0)
b_g_ = torch.cat((b_g_, b_g_neg_), 0)
b_f_ = torch.cat((b_f_, b_f_neg_), 0)
b_c_ = torch.cat((b_c_, b_c_neg_), 0)
b_hx_ = torch.cat((b_hx_, b_hx_neg_), 0)
b_t = torch.cat((b_t, b_t_neg), 0)
## actor loss
log_probs, q_eval, b_hx_from_net = self.actor_net(b_g, b_f.detach(), b_c, b_hx.detach(), b_r)
log_probs_temp = torch.zeros((b_a.shape[0], 1)).to(device)
q_eval_temp = torch.zeros((b_a.shape[0], 1)).to(device)
for i in range(b_a.shape[0]):
log_probs_temp[i][0] = log_probs[i][b_a[i][0]].to(device)
q_eval_temp[i][0] = log_probs[i][b_a[i][0]].to(device)
q_next = self.actor_net(b_g_, b_f_, b_c_, b_hx_, b_r)[1].detach()
q_target = b_r + 0.8 * q_next.max(1)[0].view(b_a.shape[0], 1)
delta = q_target - q_eval_temp
delta = b_r - q_eval_temp
# print('q_target', q_target[0], 'q_eval', q_eval_temp[0], 'delta', delta[0])
print("log_probs_temp", log_probs_temp, "b_r", b_r)
actor_loss = torch.mean(- torch.log(log_probs_temp) * b_r.detach()).float()
# critic_loss = torch.mean(delta ** 2).float()
critic_loss = 0
## supervised_loss
supervised_loss = 0
## total loss
loss = actor_loss + critic_loss + supervised_loss
self.actor_optimizer.zero_grad()
print("actor_loss", actor_loss, "critic_loss", critic_loss, "supervised_loss", supervised_loss)
loss.backward()
self.actor_optimizer.step()
# self.memory.pop(0)
self.neg_memory.pop(0)
return actor_loss, critic_loss
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