def forward_message_embedding(self, inputs, loss, training):
results = []
for i in range(self.slots):
results.append(self.local_trans(inputs[i], training=training))
results.append(self.global_trans(inputs[-1], training=training))
if self.use_comm:
for i in range(self.slots):
tmp = nd.zeros_like(results[i])
for j in range(self.slots):
msg = nd.softmax(
self.local2local_msg_encode(inputs[j],
training=training))
tmp = tmp + self.local2local_extract(
self.local2local_embedding(msg, training=training),
training=training)
msg = nd.softmax(
self.global2local_msg_encode(inputs[-1],
training=training))
tmp = tmp + self.global2local_extract(
self.global2local_embedding(msg, training=training),
training=training)
results[i] = results[i] + (tmp / float(self.slots))
tmp = nd.zeros_like(results[-1])
for i in range(self.slots):
msg = nd.softmax(
self.local2global_msg_encode(inputs[i], training=training))
tmp = tmp + self.local2global_extract(
self.local2global_embedding(msg, training=training),
training=training)
results[-1] = results[-1] + (tmp / float(self.slots))
return results
def hybrid_forward(self,
F,
input_logits,
target_logits,
sample_weight=None):
input_softmax = F.softmax(input_logits, axis=1)
target_softmax = F.softmax(target_logits, axis=1)
loss = F.square(input_softmax - target_softmax)
return F.mean(loss, axis=self._batch_axis, exclude=True)
def check_KL(self):
ph_act = nd.dot(self.enum_states, self.W) + self.hb
vt = nd.dot(self.enum_states, self.vb)
ht = nd.sum(-nd.log(nd.sigmoid(-ph_act)), axis=1)
p_th = nd.softmax(vt + ht)
KL = nd.sum(self.prob_states * nd.log(self.prob_states / p_th))
return KL.asnumpy()[0]
def inference(self):
# self-attention
x = self.embedding(1).reshape(-3, 0) # .squeeze() # b x action x h
kshape = (1, self.num_total_tokens, self.hidden_size)
vshape = (1, self.num_total_tokens, 1)
querry = self.querry(x).reshape(*kshape) # b x actions x h
key = self.key(x).reshape(*kshape) # b x actions x h
value = self.value(x).reshape(*vshape) # b x actions x 1
atten = mx.nd.linalg_gemm2(querry, key,
transpose_b=True).softmax(axis=1)
alphas = mx.nd.linalg_gemm2(atten, value).squeeze(axis=-1)
actions = []
for idx in range(len(self.num_tokens)):
i0 = sum(self.num_tokens[:idx])
i1 = sum(self.num_tokens[:idx + 1])
logits = alphas[:, i0:i1]
probs = F.softmax(logits, axis=-1)
action = mx.nd.argmax(probs, 1)
actions.append(action)
config = {}
for i, action in enumerate(actions):
choice = action.asscalar()
k, space = self.spaces[i]
config[k] = int(choice)
return config
def generate_text(model, seed, length=512, top_n=10):
"""
generates text of specified length from trained model
with given seed character sequence.
"""
logger.info("generating %s characters from top %s choices.", length, top_n)
logger.info('generating with seed: "%s".', seed)
generated = seed
encoded = mx.nd.array(encode_text(seed))
seq_len = encoded.shape[0]
x = F.expand_dims(encoded[:seq_len - 1], 1)
# input shape: [seq_len, 1]
state = model.begin_state()
# get rnn state due to seed sequence
_, state = model(x, state)
next_index = encoded[seq_len - 1].asscalar()
for i in range(length):
x = mx.nd.array([[next_index]])
# input shape: [1, 1]
logit, state = model(x, state)
# output shape: [1, vocab_size]
probs = F.softmax(logit)
next_index = sample_from_probs(probs.asnumpy().squeeze(), top_n)
# append to sequence
generated += ID2CHAR[next_index]
logger.info("generated text: \n%s\n", generated)
return generated
def test(ctx, val_data, opt, net):
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
for i, batch in enumerate(val_data):
data, label = batch_fn(batch, ctx)
outputs = []
for _, X in enumerate(data):
X = X.reshape((-1, ) + X.shape[2:])
pred = net(X.astype(opt.dtype, copy=False))
if opt.use_softmax:
pred = F.softmax(pred, axis=1)
outputs.append(pred)
acc_top1.update(label, outputs)
acc_top5.update(label, outputs)
mx.ndarray.waitall()
_, cur_top1 = acc_top1.get()
_, cur_top5 = acc_top5.get()
if i > 0 and i % opt.log_interval == 0:
print('%04d/%04d is done: acc-top1=%f acc-top5=%f' %
(i, len(val_data), cur_top1 * 100, cur_top5 * 100))
_, top1 = acc_top1.get()
_, top5 = acc_top5.get()
return (top1, top5)
def softmax_viterbi_decode(self, feats):
feats = nd.softmax(feats).asnumpy()
transitions = self.transitions.asnumpy()
label_size = len(self.tag_dictionary)
sent_len = len(feats)
pre_matrix = np.zeros((sent_len, label_size), dtype=int)
score_matrix = np.zeros((2, label_size))
score_matrix[0] = feats[0]
for i in range(1, sent_len):
_i = i & 1
_i_1 = 1 - _i
for cur_label in range(label_size):
max_score = -sys.float_info.max
for pre_label in range(label_size):
score = feats[i, cur_label]
cur_score = score_matrix[_i_1][pre_label] * transitions[pre_label, cur_label] * score
if max_score < cur_score:
max_score = cur_score
pre_matrix[i, cur_label] = pre_label
score_matrix[_i][cur_label] = max_score
last_time = (sent_len - 1) & 1
max_score = score_matrix[last_time].max()
max_index = np.argmax(score_matrix[last_time])
labels = []
for i in range(sent_len - 1, -1, -1):
labels.insert(0, max_index)
max_index = pre_matrix[i, max_index]
return labels
def forward(self, x):
x = self.embedding(nd.array(x))
x = self.bn(x)
x = self.pool(self.conv2(self.conv1(x)))
x = self.h2(self.h1(x))
return F.softmax(self.output(x))
def sample_v_given_h(self, h0):
v1_prob = self.propdown(h0).reshape([-1, self.n_val])
v1_prob = nd.softmax(v1_prob)
v1_args = nd.sample_multinomial(v1_prob)
v1 = nd.one_hot(v1_args, self.n_val)
return [
v1_prob.reshape([-1, self.n_node]),
v1.reshape([-1, self.n_node])
]
def forward(self, x):
embed = self.embed(x)
xs = []
for i in range(self.C * 2):
x = self.outs[i](embed)
x = F.softmax(x)
xs.append(x)
return xs
def forward(self, x):
#import pdb
#pdb.set_trace()
X_ = self.attn(x) # (n, w) -> (n,num_hidden)
# should be dot(X_, W)
E = self.attn(X_) # (n, hidden) -> (n, hidden)
attn_weights = F.softmax(E, axis=1) # (n, hidden)
attn_applied = F.elemwise_mul(attn_weights, X_) #(n,hidden)
output = self.c*(F.elemwise_mul(X_, attn_weights)) + (1-self.c)*X_
output = self.out(output) #(n,hidden) -> (n,output_size)
return output
def forward(self, x):
x = x / 255.
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.l1(x)
value = self.value(x)
logits = F.softmax(self.logits(x))
return value, logits
def train(self, s_batch, a_batch_one_hot, V_trace, advantage):
batch_size = s_batch.shape[0]
action_indx = np.argmax(a_batch_one_hot,axis=1).tolist()
action_stats = [action_indx.count(action_indx[i]) for i in range(batch_size)]
action_bp_rate = (1 - np.array(action_stats)/float(batch_size))**2
s_batch = copy.deepcopy(s_batch)
a_batch_one_hot = copy.deepcopy(a_batch_one_hot)
V_trace_batch = copy.deepcopy(V_trace)
advantage_batch = copy.deepcopy(advantage)
s_batch = nd.array(s_batch, ctx=CTX)
a_batch_one_hot = nd.array(a_batch_one_hot, ctx=CTX)
V_trace_batch = nd.array(V_trace_batch, ctx=CTX)
advantage_batch = nd.array(advantage_batch, ctx=CTX)
action_bp_rate = nd.softmax(nd.array(action_bp_rate, ctx=CTX))
self.actorcritic.collect_params().zero_grad()
self.reset_noise()
with mx.autograd.record():
loss_vec = []
probs, values, top_decisions = self.actorcritic.forward(s_batch, loss_vec)
loss = 0.
for element in loss_vec:
loss = loss + element
# print 'loss_dropout:', loss
logprob = nd.log(nd.sum(data=probs * a_batch_one_hot, axis=1)+1e-5)
entropy = -nd.sum(nd.sum(data=probs*nd.log(probs+1e-5), axis=1), axis=0)
top_decision_entropy = -nd.sum(nd.sum(data=top_decisions*nd.log(top_decisions+1e-5), axis=1), axis=0)
entropy_loss = - entropy
top_decision_entropy_loss = - top_decision_entropy
actorloss = -nd.sum(action_bp_rate*(logprob*advantage_batch), axis=0)
criticloss = nd.sum(action_bp_rate*nd.square(values-V_trace_batch), axis=0)
# actorloss = -nd.sum(logprob*advantage_batch, axis=0)
# criticloss = nd.sum(nd.square(values-V_trace_batch), axis=0)
loss = actorloss + 0.3*criticloss + 0.001*entropy_loss
# loss = actorloss + 0.3*criticloss + 0.0001*top_decision_entropy_loss
loss.backward()
# CTname = threading.currentThread().getName()
# print(CTname + ' actorloss : '+str(actorloss))
# print(CTname + ' criticloss : '+str(criticloss))
# print(CTname + ' entropy_loss : '+str(entropy_loss))
grads_list = []
for name, value in self.actorcritic.collect_params().items():
if name.find('batchnorm') < 0:
# grads_list.append(mx.nd.array(value.grad().asnumpy()))
grads_list.append(value.grad())
return grads_list, batch_size
def sample(self, batch_size=1, with_details=False, with_entropy=False):
"""
Returns
-------
configs : list of dict
list of configurations
"""
inputs = self.static_inputs[batch_size]
hidden = self.static_init_hidden[batch_size]
actions = []
entropies = []
log_probs = []
for idx in range(len(self.num_tokens)):
logits, hidden = self.forward(inputs,
hidden,
idx,
is_embed=(idx == 0))
probs = F.softmax(logits, axis=-1)
log_prob = F.log_softmax(logits, axis=-1)
entropy = -(log_prob *
probs).sum(1, keepdims=False) if with_entropy else None
action = mx.random.multinomial(probs, 1)
ind = mx.nd.stack(mx.nd.arange(probs.shape[0], ctx=action.context),
action.astype('float32'))
selected_log_prob = F.gather_nd(log_prob, ind)
actions.append(action[:, 0])
entropies.append(entropy)
log_probs.append(selected_log_prob)
inputs = action[:, 0] + sum(self.num_tokens[:idx])
inputs.detach()
configs = []
for idx in range(batch_size):
config = {}
for i, action in enumerate(actions):
choice = action[idx].asscalar()
k, space = self.spaces[i]
config[k] = int(choice)
configs.append(config)
if with_details:
entropies = F.stack(*entropies,
axis=1) if with_entropy else entropies
return configs, F.stack(*log_probs, axis=1), entropies
else:
return configs
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool2(F.relu(self.conv2(x)))
x = self.drop2D(x)
# 0 means copy over size from corresponding dimension.
# -1 means infer size from the rest of dimensions.
# Essentially flattens to 1D.
x = x.reshape((0, -1))
x = F.relu(self.fc1(x))
x = self.drop1D(x)
x = F.relu(self.fc2(x))
x = F.softmax(x)
return x
def _update(self):
# Train
if self.frame_counter > self.opt.replay_start_size and \
self.frame_counter % self.opt.learning_frequency == 0:
batch_reward, batch_action, batch_done = self.replay_memory.sample(
self.opt, self.batch_state, self.batch_state_next)
batch_reward, batch_action, batch_done = batch_reward.asnumpy(), \
batch_action.asnumpy(), batch_done.asnumpy()
targets_q = self.dqn(self.batch_state_next).asnumpy()
targets_q = np.reshape(
targets_q, (targets_q.shape[0], self.num_action, self.atoms))
q_values = np.dot(targets_q, self.z_values)
target_actions = np.argmax(q_values, axis=1).astype('int32')
value_eval = self.target_dqn(self.batch_state_next).asnumpy()
value_eval = np.reshape(
value_eval, (value_eval.shape[0], self.num_action, self.atoms))
distributed_q = value_eval[:, target_actions, :]
m = np.zeros((self.opt.batch_size, self.z_values.size))
for j in range(self.atoms):
tzj = np.fmax(
np.fmin(
batch_reward -
batch_done * self.opt.gamma * self.z_values[j],
self.v_max), self.v_min)
bj = ((tzj - self.z_values[0]) /
(self.z_values[1] - self.z_values[0]))
u = np.ceil(bj).astype('int32')
l = np.floor(bj).astype('int32')
m[:,
l] = m[:, l] + distributed_q[:, target_actions, j] * (u - bj)
m[:,
u] = m[:, u] + distributed_q[:, target_actions, j] * (bj - l)
m = F.softmax(nd.array(m, self.opt.ctx))
with autograd.record():
TD_targets = nd.reshape(
self.dqn(self.batch_state),
(self.opt.batch_size, self.num_action, self.atoms))
TD_targets_action = TD_targets[self.batches, batch_action]
loss = self.cross_ent_loss(TD_targets_action, m)
loss.backward()
self.trainer.step(self.opt.batch_size)
if self.frame_counter % 800 == 0:
print('Loss is', nd.sum(loss).asscalar())
def sample(self, batch_size=1, with_details=False, with_entropy=False):
# self-attention
x = self.embedding(batch_size).reshape(
-3, 0) # .squeeze() # b x action x h
kshape = (batch_size, self.num_total_tokens, self.hidden_size)
vshape = (batch_size, self.num_total_tokens, 1)
querry = self.querry(x).reshape(*kshape) # b x actions x h
key = self.key(x).reshape(*kshape) # b x actions x h
value = self.value(x).reshape(*vshape) # b x actions x 1
atten = mx.nd.linalg_gemm2(querry, key,
transpose_b=True).softmax(axis=1)
alphas = mx.nd.linalg_gemm2(atten, value).squeeze(axis=-1)
actions = []
entropies = []
log_probs = []
for idx in range(len(self.num_tokens)):
i0 = sum(self.num_tokens[:idx])
i1 = sum(self.num_tokens[:idx + 1])
logits = alphas[:, i0:i1]
probs = F.softmax(logits, axis=-1)
log_prob = F.log_softmax(logits, axis=-1)
entropy = -(log_prob *
probs).sum(1, keepdims=False) if with_entropy else None
action = mx.random.multinomial(probs, 1)
ind = mx.nd.stack(mx.nd.arange(probs.shape[0], ctx=action.context),
action.astype('float32'))
selected_log_prob = F.gather_nd(log_prob, ind)
actions.append(action[:, 0])
entropies.append(entropy)
log_probs.append(selected_log_prob)
configs = []
for idx in range(batch_size):
config = {}
for i, action in enumerate(actions):
choice = action[idx].asscalar()
k, space = self.spaces[i]
config[k] = int(choice)
configs.append(config)
if with_details:
entropies = F.stack(*entropies,
axis=1) if with_entropy else entropies
return configs, F.stack(*log_probs, axis=1), entropies
else:
return configs
def GRU_Cell(input, state):
for x in input:
z_t = nd.Activation(nd.FullyConnected(data=x,weight=wxz,no_bias=True,num_hidden=num_hidden)+
nd.FullyConnected(data=state,weight=whz,no_bias=True,num_hidden=num_hidden)+bz,act_type="sigmoid")
r_t = nd.Activation(nd.FullyConnected(data=x,weight=wxr,no_bias=True,num_hidden=num_hidden)+
nd.FullyConnected(data=state,weight=whr,no_bias=True,num_hidden=num_hidden)+br,act_type="sigmoid")
g_t = nd.Activation(nd.FullyConnected(data=x,weight=wxh,no_bias=True,num_hidden=num_hidden)+
nd.FullyConnected(data=r_t*state,weight=whh,no_bias=True,num_hidden=num_hidden)+bh,act_type="tanh")
state = nd.multiply(z_t,state) + nd.multiply(1-z_t,g_t)
output = nd.FullyConnected(data=state, weight=why, bias=by, num_hidden=num_outputs)
output = nd.softmax(data=output)
return output, state
def transform_fn(model, request_body, content_type, accept_type):
try:
input_object=json.loads(request_body)
board=input_object["board"]
count=input_object["session"].get("count",0)
input_object["session"]["count"]=count+1
if count>10:
board=nd.array(board)
board=nd.concat(
(board==1).expand_dims(axis=0),
(board==2).expand_dims(axis=0),dim=0
)
board=board.expand_dims(axis=0)
mask=board.clip(0,1)
mask=-(mask-1)
mask=mask.reshape((2,-1))
p=nd.softmax(model(board).reshape((-1,)))
p=p*mask[0]*mask[1]
while True:
loc=int(p.argmax(axis=0).asscalar())
y=loc//board.shape[2]
x=loc%board.shape[2]
if input_object["board"][y][x]==0:
break
else:
p[loc]=0
else:
while True:
x=random.randint(0,len(input_object["board"][0])-1)
y=random.randint(0,len(input_object["board"])-1)
if input_object["board"][y][x]==0:
break
input_object["session"]["shootType"]="CNNNet"
return bytearray(json.dumps({
"shot":{
"x":x,
"y":y
},
"session":input_object["session"]
}),'utf-8'),accept_type
except Exception as e:
print(traceback.format_exc())
print(e)
def inference(self):
actions = []
for idx in range(len(self.num_tokens)):
logits = self.decoders[idx](1)
probs = F.softmax(logits, axis=-1)
action = mx.nd.argmax(probs, 1)
actions.append(action)
config = {}
for i, action in enumerate(actions):
choice = action.asscalar()
k, space = self.spaces[i]
config[k] = int(choice)
return config
def forward(self, x):
#x: 'nwc'
#import pdb
#pdb.set_trace()
x = F.transpose(x, axes=(0, 2, 1)) # (nwc) -> (ncw)
X_ = F.batch_dot(self.w1.data(ctx), x) # (n,c,w) -> (n,c,w)
# E = dot(X_, W)
E = F.batch_dot(X_, self.w.data(ctx)) # (n,c,w) -> (n,c,w)
attn_weights = F.softmax(E, axis=2) # (n, c, w)
attn_applied = F.elemwise_mul(attn_weights, X_) #(n,c,w)
output = self.c.data(ctx) * (attn_applied) + (
1 - self.c.data(ctx)) * X_ # (n,c,w)
output = F.batch_dot(output, self.w2.data(ctx)) + self.b.data(
ctx) # (n, c,w)
output = F.transpose(output, axes=(0, 2, 1)) # (ncw) -> (nwc)
return output
def forward(self, x):
# x=self.fc1(x)
#print(x.shape)
#print(self.l.shape)
out = broad_multiply(x, self.l, self.ctx)
# print(out.shape)
out = self.avgpool(out)
# print(out.shape)
out2 = self.fc(out)
# print(out2.shape)
out3 = nd.softmax(out2)
out = out2 * out3
# print(out.shape)
# print(x.shape[0],self.l.shape[1])
out = out.reshape((x.shape[0], self.l.shape[1], -1))
# print(out.shape)
out = nd.sum(out, axis=1)
# print(out.shape)
return out
def inference(self):
inputs = self.static_inputs[1]
hidden = self.static_init_hidden[1]
actions = []
for block_idx in range(len(self.num_tokens)):
logits, hidden = self.forward(inputs, hidden,
block_idx, is_embed=(block_idx==0))
probs = F.softmax(logits, axis=-1)
action = mx.nd.argmax(probs, 1)
actions.append(action)
inputs = action + sum(self.num_tokens[:block_idx])
inputs.detach()
config = {}
for i, action in enumerate(actions):
choice = action.asscalar()
k, space = self.spaces[i]
config[k] = int(choice)
return config
def forward(self, X, stride=1):
filters = []
for i in range(self._n_scales):
kernel = (i * 2 + 1, ) * 2
pad = (i, ) * 2
f = nd.Pooling(data=data,
pool_type='max',
kernel=kernel,
stride=(stride, stride),
pad=pad,
cudnn_off=True)
f = nd.reshape(f, (f.shape[0], 1) + f.shape[1:])
filters.append(f)
filters = nd.concat(*filters, dim=1)
weight = nd.softmax(self._get_param(self.weight), axis=1)
filters = nd.mean(filters, axis=1)
# filters = nd.sum(filters * weight, axis=1)
return filters
def train_update(self, s_batch, a_batch_one_hot, V_trace, advantage):
batch_size = s_batch.shape[0]
action_indx = np.argmax(a_batch_one_hot, axis=1).tolist()
action_stats = [action_indx.count(action_indx[i]) for i in range(batch_size)]
action_bp_rate = (1 - np.array(action_stats) / float(batch_size)) ** 2
s_batch = copy.deepcopy(s_batch)
a_batch_one_hot = copy.deepcopy(a_batch_one_hot)
V_trace_batch = copy.deepcopy(V_trace)
advantage_batch = copy.deepcopy(advantage)
s_batch = nd.array(s_batch, ctx=CTX)
a_batch_one_hot = nd.array(a_batch_one_hot, ctx=CTX)
V_trace_batch = nd.array(V_trace_batch, ctx=CTX)
advantage_batch = nd.array(advantage_batch, ctx=CTX)
action_bp_rate = nd.softmax(nd.array(action_bp_rate, ctx=CTX))
self.actorcritic.collect_params().zero_grad()
self.reset_noise()
with mx.autograd.record():
loss_vec = []
probs, values, top_decisions = self.actorcritic.forward(s_batch, loss_vec)
loss = 0.
for element in loss_vec:
loss = loss + element
# print 'loss_dropout:', loss
logprob = nd.log(nd.sum(data=probs * a_batch_one_hot, axis=1) + 1e-5)
entropy = -nd.sum(nd.sum(data=probs * nd.log(probs + 1e-5), axis=1), axis=0)
top_decision_entropy = -nd.sum(nd.sum(data=top_decisions * nd.log(top_decisions + 1e-5), axis=1), axis=0)
entropy_loss = - entropy
top_decision_entropy_loss = - top_decision_entropy
actorloss = -nd.sum(action_bp_rate * (logprob * advantage_batch), axis=0)
criticloss = nd.sum(action_bp_rate * nd.square(values - V_trace_batch), axis=0)
# actorloss = -nd.sum(logprob*advantage_batch, axis=0)
# criticloss = nd.sum(nd.square(values-V_trace_batch), axis=0)
loss = actorloss + 0.3 * criticloss + 0.001 * entropy_loss
# loss = actorloss + 0.3*criticloss + 0.0001*top_decision_entropy_loss
loss.backward()
self.trainer.step(batch_size=batch_size, ignore_stale_grad=True)
def forward(self, input, hidden, encoder_outputs):
#input shape, (1,)
embedded = self.embedding(input)
if self.dropout_p > 0:
embedded = self.dropout(embedded)
attn_weights = F.softmax(
self.attn(F.concat(embedded, hidden[0].flatten(), dim=1)))
attn_applied = F.batch_dot(attn_weights.expand_dims(0),
encoder_outputs.expand_dims(0))
output = F.concat(embedded.flatten(), attn_applied.flatten(), dim=1)
output = self.attn_combine(output).expand_dims(0)
for i in range(self.n_layers):
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = self.out(output)
return output, hidden, attn_weights
def forward(self, input, hidden, encoder_outputs):
#input shape, (1,)
embedded = self.embedding(input)
if self.dropout_p > 0:
embedded = self.dropout(embedded)
attn_weights = F.softmax(
self.attn(F.concat(embedded, hidden[0].flatten(), dim=1)))
attn_applied = F.batch_dot(attn_weights.expand_dims(0),
encoder_outputs.expand_dims(0))
output = F.concat(embedded.flatten(), attn_applied.flatten(), dim=1)
output = self.attn_combine(output).expand_dims(0)
for i in range(self.n_layers):
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = self.out(output)
return output, hidden, attn_weights
def test(ctx, val_data, opt, net):
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
true_labels = []
predictions = []
for i, batch in enumerate(val_data):
data, label = batch_fn(batch, ctx)
outputs = []
for _, X in enumerate(data):
X = X.reshape((-1, ) + X.shape[2:])
# pred = net(X.astype(opt.dtype, copy=False))
pred = net(X)
if opt.use_softmax:
pred = F.softmax(pred, axis=1)
outputs.append(pred)
predictions.append(outputs)
true_labels.append(label)
acc_top1.update(label, outputs)
acc_top5.update(label, outputs)
mx.ndarray.waitall()
_, cur_top1 = acc_top1.get()
_, cur_top5 = acc_top5.get()
if i > 0 and i % opt.log_interval == 0:
print('%04d/%04d is done: acc-top1=%f acc-top5=%f' %
(i, len(val_data), cur_top1 * 100, cur_top5 * 100))
_, top1 = acc_top1.get()
_, top5 = acc_top5.get()
#save true_labels, predictions
predictions = _list_to_numpy(predictions)
true_labels = _list_to_numpy(true_labels)
np.save(os.path.join(opt.save_dir, "labels"), true_labels)
np.save(os.path.join(opt.save_dir, "predictions"), predictions)
return top1, top5, true_labels, predictions
def LSTM_Cell(input, h_state, c_state):
for x in input:
f_t = nd.Activation(nd.FullyConnected(
data=x, weight=wxhf, no_bias=True, num_hidden=num_hidden) +
nd.FullyConnected(data=h_state,
weight=whhf,
no_bias=True,
num_hidden=num_hidden) + bhf,
act_type="sigmoid")
i_t = nd.Activation(nd.FullyConnected(
data=x, weight=wxhi, no_bias=True, num_hidden=num_hidden) +
nd.FullyConnected(data=h_state,
weight=whhi,
no_bias=True,
num_hidden=num_hidden) + bhi,
act_type="sigmoid")
o_t = nd.Activation(nd.FullyConnected(
data=x, weight=wxho, no_bias=True, num_hidden=num_hidden) +
nd.FullyConnected(data=h_state,
weight=whho,
no_bias=True,
num_hidden=num_hidden) + bho,
act_type="sigmoid")
g_t = nd.Activation(nd.FullyConnected(
data=x, weight=wxhg, no_bias=True, num_hidden=num_hidden) +
nd.FullyConnected(data=h_state,
weight=whhg,
no_bias=True,
num_hidden=num_hidden) + bhg,
act_type="tanh")
c_state = nd.multiply(f_t, c_state) + nd.multiply(i_t, g_t)
h_state = nd.multiply(o_t, nd.tanh(c_state))
output = nd.FullyConnected(data=h_state,
weight=why,
bias=by,
num_hidden=num_outputs)
output = nd.softmax(data=output)
return output, h_state, c_state
def forward(self, is_train, req, in_data, out_data, aux):
arm_cls_preds = in_data[0]
odm_cls_target = in_data[1]
odm_loc_target_mask = in_data[2]
arm_cls_preds = nd.softmax(data=arm_cls_preds)
arm_cls_preds_classes = nd.split(data=arm_cls_preds,axis=1,num_outputs=2)
# arm_cls_preds_bg shape : (batch , h*w*num_anchors[:layers]) 负类【0】
arm_cls_preds_bg = nd.reshape(data=arm_cls_preds_classes[0],shape=(0,-1))
prob_temp = nd.ones_like(arm_cls_preds_bg)*0.99
cond1 = arm_cls_preds_bg >= prob_temp # > 0.99 idx is 1
# print('negative cond1 ------- :',heapq.nlargest(2,arm_cls_preds_bg[0]))
temp1 = nd.ones_like(odm_cls_target)*(-1) ### TODO： 0 还是-1表示背景??
# 如果ARM分类出的负类的置信度大于0.99，将其在ODM的anchor标号中去掉（-1替代），负类转换为背景
odm_cls_target_mask = nd.where(condition=cond1,x=temp1,y=odm_cls_target)
# apply filtering to odm_loc_target_mask
# odm_loc_target_mask_shape: (batch, num_anchors, 4)
arm_cls_preds_bg = nd.reshape(data=arm_cls_preds_bg,shape=(0,-1,1))#(batch , h*w*num_anchors[:layers],1)
# (batch , h*w*num_anchors[:layers] , 4 )
odm_loc_target_mask = nd.reshape(data=odm_loc_target_mask,shape=(0,-1,4))
odm_loc_target_mask = odm_loc_target_mask[:,:,0] #(batch , h*w*num_anchors[:layers])
#(batch , h*w*num_anchors[:layers], 1)
## 取整个batch中 所有行的 第一列，相当于对原来的4个相同label[0 0 0 0 ],[1 1 1 1]变成[0],[1]
odm_loc_target_mask = nd.reshape(data=odm_loc_target_mask,shape=(0,-1,1))
loc_temp = nd.ones_like(odm_loc_target_mask)*0.99
cond2 = arm_cls_preds_bg >= loc_temp
temp2 = nd.zeros_like(odm_loc_target_mask) # 取0
# 如果ARM分类出的负类的置信度大于0.99，将其在ODM的掩码置0
## 实际上不管IOU计算的大小，用AMR的分类结果，如果是大于0.99的负类，不管通过IOU判断的正负类结果如何，都设置为背景
odm_loc_target_bg_mask = nd.where(cond2,temp2,odm_loc_target_mask)
odm_loc_target_bg_mask = nd.concat(*[odm_loc_target_bg_mask]*4,dim=2)
# 还原维度
odm_loc_target_bg_mask = nd.reshape(odm_loc_target_bg_mask,shape=(0,-1))
for ind, val in enumerate([odm_cls_target_mask, odm_loc_target_bg_mask]):
self.assign(out_data[ind], req[ind], val)
def sample(self, batch_size=1, with_details=False, with_entropy=False):
actions = []
entropies = []
log_probs = []
for idx in range(len(self.num_tokens)):
logits = self.decoders[idx](batch_size)
probs = F.softmax(logits, axis=-1)
log_prob = F.log_softmax(logits, axis=-1)
entropy = -(log_prob *
probs).sum(1, keepdims=False) if with_entropy else None
action = mx.random.multinomial(probs, 1)
ind = mx.nd.stack(mx.nd.arange(probs.shape[0], ctx=action.context),
action.astype('float32'))
selected_log_prob = F.gather_nd(log_prob, ind)
actions.append(action[:, 0])
entropies.append(entropy)
log_probs.append(selected_log_prob)
configs = []
for idx in range(batch_size):
config = {}
for i, action in enumerate(actions):
choice = action[idx].asscalar()
k, space = self.spaces[i]
config[k] = int(choice)
configs.append(config)
if with_details:
entropies = F.stack(*entropies,
axis=1) if with_entropy else entropies
return configs, F.stack(*log_probs, axis=1), entropies
else:
return configs
def forward(self, x):
x = self.dense(x)
probs = self.action_pred(x)
values = self.value_pred(x)
return F.softmax(probs), values
# 5. Test the networks
total_batch = int(np.ceil(X_test.shape[0] / batch_size))
correct_counts = [0 for i in range(num_models)]
ensemble_correct_count = 0
total_num = 0
for i in range(total_batch):
num_valid = batch_size if (i + 1) * batch_size <= X_test.shape[0]\
else X_test.shape[0] - i * batch_size
data_npy, label_npy, num_valid = get_batch(i, batch_size, X_test, y_test)
prob_ensemble = nd.zeros(shape=(label_npy.shape[0], 10), ctx=mx.gpu())
for i, test_net in enumerate(test_nets):
test_net.forward(data_batch=mx.io.DataBatch(data=[nd.array(data_npy)],
label=None),
is_train=False)
logits_nd = test_net.get_outputs()[0]
prob_nd = nd.softmax(logits_nd)
prob_ensemble += prob_nd
pred_cls = nd.argmax(prob_nd, axis=-1).asnumpy()
correct_counts[i] += (pred_cls[:num_valid] == label_npy[:num_valid]).sum()
prob_ensemble /= num_models
ensemble_pred_cls = nd.argmax(prob_ensemble, axis=-1).asnumpy()
ensemble_correct_count += (ensemble_pred_cls[:num_valid] == label_npy[:num_valid]).sum()
for i in range(num_models):
print(i, 'Accuracy:', correct_counts[i] / float(X_test.shape[0]))
print('Ensemble accuracy:', ensemble_correct_count / float(X_test.shape[0]))
'''
Learning Started!
Epoch: 0001 cost = [ 0.23813407 0.23717315]
Epoch: 0002 cost = [ 0.07455271 0.07434764]
Epoch: 0003 cost = [ 0.05925059 0.06024327]
Epoch: 0004 cost = [ 0.05032205 0.04895757]