def backward(dY, cache):
Xe = cache['Xe']
generator_str = cache['generator_str']
dWs = np.zeros(cache['Ws_shape'])
gen_caches = cache['gen_caches']
F = cache['F']
dXe = np.zeros(Xe.shape)
Generator = decodeGenerator(generator_str)
# backprop each item in the batch
grads = {}
for i in xrange(len(gen_caches)):
ix, gen_cache = gen_caches[i] # unpack
local_grads = Generator.backward(dY[i], gen_cache)
dXs = local_grads['dXs'] # intercept the gradients wrt Xi and Xs
del local_grads['dXs']
dXi = local_grads['dXi']
del local_grads['dXi']
accumNpDicts(
grads,
local_grads) # add up the gradients wrt model parameters
# now backprop from dXs to the image vector and word vectors
dXe[i, :] += dXi # image vector
for n, j in enumerate(ix): # and now all the other words
dWs[j, :] += dXs[n, :]
# finally backprop into the image encoder
dWe = F.transpose().dot(dXe)
dbe = np.sum(dXe, axis=0, keepdims=True)
accumNpDicts(grads, {'We': dWe, 'be': dbe, 'Ws': dWs})
return grads
def backward(dY, cache):
Xe = cache['Xe']
generator_str = cache['generator_str']
dWs = np.zeros(cache['Ws_shape'])
gen_caches = cache['gen_caches']
F = cache['F']
dXe = np.zeros(Xe.shape)
Generator = decodeGenerator(generator_str)
# backprop each item in the batch
grads = {}
for i in xrange(len(gen_caches)):
ix, gen_cache = gen_caches[i] # unpack
local_grads = Generator.backward(dY[i], gen_cache)
dXs = local_grads['dXs'] # intercept the gradients wrt Xi and Xs
del local_grads['dXs']
dXi = local_grads['dXi']
del local_grads['dXi']
accumNpDicts(grads, local_grads) # add up the gradients wrt model parameters
# now backprop from dXs to the image vector and word vectors
dXe[i,:] += dXi # image vector
for n,j in enumerate(ix): # and now all the other words
dWs[j,:] += dXs[n,:]
# finally backprop into the image encoder
dWe = F.transpose().dot(dXe)
dbe = np.sum(dXe, axis=0, keepdims = True)
accumNpDicts(grads, { 'We':dWe, 'be':dbe, 'Ws':dWs })
return grads
def backward(dY, cache):
Xe = cache['Xe']
generator_str = cache['generator_str']
dWs = np.zeros(cache['Ws_shape'])
gen_caches = cache['gen_caches']
F = cache['F']
dXe = np.zeros(Xe.shape)
Generator = decodeGenerator(generator_str)
dmmy, gen_cache = gen_caches[0]
g_WLSTM = cuda.to_device(np.asfortranarray(gen_cache['WLSTM']))
# backprop each item in the batch
grads = {}
dt1 = 0
dt2 = 0
t0 = time.time()
for i in xrange(len(gen_caches)):
t1 = time.time()
ix, gen_cache = gen_caches[i] # unpack
local_grads = Generator.backward(dY[i], gen_cache, g_WLSTM)
dt1 += time.time() - t1
t2 = time.time()
dXs = local_grads['dXs'] # intercept the gradients wrt Xi and Xs
del local_grads['dXs']
dXi = local_grads['dXi']
del local_grads['dXi']
accumNpDicts(
grads,
local_grads) # add up the gradients wrt model parameters
# now backprop from dXs to the image vector and word vectors
dXe[i, :] += dXi # image vector
for n, j in enumerate(ix): # and now all the other words
dWs[j, :] += dXs[n, :]
dt2 += time.time() - t2
#dt = time.time() - t0
#print 'BP :%0.4f' %(dt)
dt = time.time() - t0
print 'Backward Pass:%0.4f Others :%0.4f' % (dt1, dt2)
t0 = time.time()
# finally backprop into the image encoder
dWe = F.transpose().dot(dXe)
dbe = np.sum(dXe, axis=0, keepdims=True)
dt = time.time() - t0
print 'MMult :%0.4f' % (dt)
t0 = time.time()
accumNpDicts(grads, {'We': dWe, 'be': dbe, 'Ws': dWs})
dt = time.time() - t0
print 'accum 2:%0.4f' % (dt)
t0 = time.time()
return grads
def backward(dY, cache):
generator_str = cache['generator_str']
gen_caches = cache['gen_caches']
X_v1_orig = cache['X_v1_orig']
X_v2_orig = cache['X_v2_orig']
X_v3_orig = cache['X_v3_orig']
We_v1 = cache['We_v1']
dWe_v1 = np.zeros(We_v1.shape)
be_v1 = cache['be_v1']
dbe_v1 = np.zeros(be_v1.shape)
We_v2 = cache['We_v2']
dWe_v2 = np.zeros(We_v2.shape)
be_v2 = cache['be_v2']
dbe_v2 = np.zeros(be_v2.shape)
We_v3 = cache['We_v3']
dWe_v3 = np.zeros(We_v3.shape)
be_v3 = cache['be_v3']
dbe_v3 = np.zeros(be_v3.shape)
Generator = decodeGenerator(generator_str)
# backprop each item in the batch
grads = {}
for i in xrange(len(gen_caches)):
ix, gen_cache = gen_caches[i] # unpack
local_grads = Generator.backward(dY[i], gen_cache)
dX_v1 = local_grads['dX_v1']
dX_v2 = local_grads['dX_v2']
dX_v3 = local_grads['dX_v3']
del local_grads['dX_v1']
del local_grads['dX_v2']
del local_grads['dX_v3']
accumNpDicts(
grads,
local_grads) # add up the gradients wrt model parameters
dWe_v1 += X_v1_orig[i].dot(dX_v1)
dbe_v1 += np.sum(dX_v1, axis=0, keepdims=True)
dWe_v2 += X_v2_orig[i].dot(dX_v2)
dbe_v2 += np.sum(dX_v2, axis=0, keepdims=True)
dWe_v3 += X_v3_orig[i].dot(dX_v3)
dbe_v3 += np.sum(dX_v3, axis=0, keepdims=True)
accumNpDicts(
grads, {
'We_v1': dWe_v1,
'We_v2': dWe_v2,
'We_v3': dWe_v3,
'be_v1': dbe_v1,
'be_v2': dbe_v2,
'be_v3': dbe_v3
})
return grads
