def load_encoder(images, MP, nbatch, param_name="encoder_params_best"):
lat_mean_layer, lat_var_layer, out = enc.make_encoder(
[nbatch, MP["datsize"]], MP["n_lat"], MP["MVG"], MP["n_gauss_dim"],
MP["CNN"])
#load parameters
enc_parameters = utils.load_obj(MP["dirname"] + param_name)
L.layers.set_all_param_values(out, enc_parameters)
if MP["MVG"]:
lat_var = [
L.layers.get_output(k, inputs=images) for k in lat_var_layer
]
else:
lat_var = L.layers.get_output(lat_var_layer, inputs=images)
var, trans = variance.get_var_mat(lat_var, MP["MVG"])
mean = L.layers.get_output(lat_mean_layer, inputs=images)
#so I need to get the variance and mean
lfunc = theano.function([images], [mean, var, trans],
allow_input_downcast=True)
return lfunc
def encoder_response_function(images,
MP,
nbatch,
param_name="encoder_params_best"):
lat_mean_layer, lat_var_layer, out = enc.make_encoder(
[nbatch, MP["datsize"]], MP["n_lat"], MP["MVG"], MP["n_gauss_dim"],
MP["CNN"])
#load parameters
enc_parameters = utils.load_obj(MP["dirname"] + param_name)
L.layers.set_all_param_values(out, enc_parameters)
intermediate = L.layers.get_all_layers(lat_mean_layer)
int_resp = [L.layers.get_output(l, inputs=images) for l in intermediate]
lfunc = theano.function([images],
int_resp,
allow_input_downcast=True,
on_unused_input='ignore')
return lfunc, len(int_resp)
def run_fcnn_model(param_dict,
generators,
opts,
opt_key,
models,
eta,
pandas_save=True,
save_weights=True,
epoch_batch_size=10):
assert (epoch_batch_size >= 1)
model_key = "fcnn"
if (param_dict["run_verbosity"] > 0):
print("Using hidden size {} and optimizer {}...".format(
param_dict["hidden_size"], opt_key))
# Set up the input to accept FMA images:
inp = keras.layers.Input(shape=(
256,
256,
3,
))
# Add a flatten layer to make the input play nicely with these non-convolutional
# layers:
x = keras.layers.Flatten()(inp)
# Add a Flatten/Affine/BatchNorm/ReLU/Dropout/Affine-softmax-categorization block:
predict = stack_two_layer_block(param_dict, x)
# Construct the model:
models[model_key] = keras.models.Model(inp, predict)
# Compile the model
models[model_key].compile(optimizer=opts[opt_key],
**param_dict["compile_args"])
fcnn_pass_epochs = param_dict[
"pass_epochs"] * 6 # Because all the other networks
# train in multiple passes
# Train the model:
timer.tic()
run_started = timer.datetimestamp()
if (param_dict["run_verbosity"] > 0):
print(("Fully connected network run begun at {}."
"\n\t[{} epochs on {} FMA on {} takes"
"\n\t{}.]\n").format(run_started, fcnn_pass_epochs,
param_dict["which_size"],
param_dict["spu"].upper(), eta))
initial_epoch = 0
results = None
# This loop of multiple checkpoints helps with memory management, which is
# probably not necessary for FCNN but is included just in case - see also
# http://bit.ly/2hDHJay for more information.
while initial_epoch < fcnn_pass_epochs:
# Split into "epoch_batch_size"-epoch training batches
final_epoch = min(initial_epoch + epoch_batch_size, fcnn_pass_epochs)
if (param_dict["run_verbosity"] > 0):
print("\nTraining for epochs {} to {}...".format(
initial_epoch + 1, final_epoch))
results_new = models[model_key].fit_generator(
generators["train"],
validation_data=generators["val"],
verbose=param_dict["run_verbosity"],
epochs=final_epoch,
steps_per_epoch=param_dict["steps_per_epoch"],
validation_steps=param_dict["validation_steps"],
use_multiprocessing=True,
initial_epoch=initial_epoch)
# Merge these new results with existing results for previous batches:
if results is not None:
# Merge the two results lists:
for key in results.history:
results.history[key].extend(results_new.history[key])
else:
results = results_new
# Now start from where we stopped on this round
initial_epoch = final_epoch
runsec = timer.toc()
# Create a new row for these results:
if (pandas_save):
new_res = pd.Series()
assign_run_key(new_res, param_dict, run_started)
assign_opt_key(new_res, opt_key)
train_acc, val_acc = assign_results_history(new_res, model_key, runsec,
results)
# Add this to the results dataframe:
try:
fma_results = ut.load_obj(param_dict["fma_results_name"])
except:
fma_results = pd.DataFrame(dtype=float, columns=RESULTS_COLS)
fma_results = fma_results.append(new_res, ignore_index="True")
# And save:
ut.save_obj(fma_results, param_dict["fma_results_name"])
else:
train_acc = results.history["categorical_accuracy"][-1]
val_acc = results.history["val_categorical_accuracy"][-1]
if (param_dict["run_verbosity"] > 0):
print(("\n{} for {} to yield {:0.1%} training accuracy "
"and {:0.1%} validation accuracy in {:d} \nepochs "
"(x3 training phases).").format(
timer.time_from_sec(runsec),
param_dict["model_names"][model_key], train_acc, val_acc,
param_dict["pass_epochs"]))
# Save trained weights:
if save_weights:
weights_save_name = os.path.join(
"saved_weights",
"{}_{}_{}_{}.h5".format(
model_key,
formatted(opt_key), # 4 elements
formatted(run_key(param_dict, run_started)),
timer.datetimepath()))
ut.ensure_dir(weights_save_name)
models[model_key].save_weights(weights_save_name)
if (param_dict["run_verbosity"] > 0):
print("\nFully connected run complete at {}.".format(
timer.datetimestamp()))
# Tell keras to clear the the tensorflow backend session (helps with memory leaks;
# see: http://bit.ly/2xJZbAt )
if param_dict[
"run_verbosity"] > 0: # i.e. this is not a short-running/crossval run--
# can't reset during crossval because tensorflow
# will get cross about the optimizer being
# created on a different graph...
print("Clearing keras's backend Tensorflow session...\n")
K.clear_session()
if (pandas_save):
return new_res
def run_pretrained_model(param_dict,
generators,
models,
opts,
model_class,
model_key,
opt_key,
print_layers,
freeze_to,
eta,
save_weights=True,
epoch_batch_size=5):
assert (epoch_batch_size >= 1)
try:
len(freeze_to)
except:
freeze_to = [freeze_to] # turn single elements into a one-item list
print("Using optimizer {}...".format(opt_key))
timer.tic()
run_started = timer.datetimestamp()
print(("{} run begun at {}."
"\n\t[{} epochs (x{} passes) on {} FMA on {} takes"
"\n\t{}.]\n").format(param_dict["model_names"][model_key],
run_started, param_dict["pass_epochs"],
len(freeze_to) + 1, param_dict["which_size"],
param_dict["spu"].upper(), eta))
# Adapted from https://keras.io/applications/
# Get the pre-trained base model, without the top layer (because our input is a
# different shape), using the trained weights for ImageNet, to use as a starting
# point:
basemodel = model_class(include_top=False,
input_shape=param_dict["mean_img"].shape,
weights='imagenet')
x = basemodel.output
# Add a global spatial average pooling layer at the output for regularization and
# to reduce overfitting:
x = keras.layers.GlobalAveragePooling2D()(x)
# Add Affine/BatchNorm/ReLU/Dropout/Affine-softmax-categorization block:
predict = stack_two_layer_block(param_dict, x)
# Now make the model:
models[model_key] = keras.models.Model(basemodel.input, predict)
# Train only the top layers (which were randomly initialized) while freezing
# all convolutional layers (which were pretrained on ImageNet):
for layer in basemodel.layers:
layer.trainable = False
# Compile the model (must be done after setting layer trainability):
models[model_key].compile(optimizer=opts[opt_key],
**param_dict["compile_args"])
# Train just the classifier for the requested number of epochs:
print("First-round training (training the classifier)...")
initial_epoch = 0
results = None
# This loop of multiple checkpoints helps with memory management, esp. for VGG16/
# VGG19, which have a huge number of parameters - see also
# http://bit.ly/2hDHJay for more information.
while initial_epoch < param_dict["pass_epochs"]:
# Split into "epoch_batch_size"-epoch training batches
final_epoch = min(initial_epoch + epoch_batch_size,
param_dict["pass_epochs"])
print("\nTraining for epochs {} to {}...".format(
initial_epoch + 1, final_epoch))
results_new = models[model_key].fit_generator(
generators["train"],
validation_data=generators["val"],
verbose=param_dict["run_verbosity"],
epochs=final_epoch,
steps_per_epoch=param_dict["steps_per_epoch"],
validation_steps=param_dict["validation_steps"],
use_multiprocessing=True,
initial_epoch=initial_epoch)
# Merge these new results with existing results for previous batches:
if results is not None:
# Merge the two results lists:
for key in results.history:
results.history[key].extend(results_new.history[key])
else:
results = results_new
# Now start from where we stopped on this round
initial_epoch = final_epoch
# At this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from Xception. We will freeze the bottom N layers
# and train the remaining top layers.
# Visualize layer names and layer indices to see how many layers we should freeze:
if print_layers:
for i, layer in enumerate(models[model_key].layers):
print(i, layer.name)
pass_num = 1
for freeze in freeze_to:
pass_num += 1
# Freeze all layers up to the specified value; unfreeze everything
# after (and including):
for layer in models[model_key].layers[:freeze]:
layer.trainable = False
for layer in models[model_key].layers[freeze:]:
layer.trainable = True
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate because SGD trains more slowly than RMSprop
# (a good thing, in this case):
models[model_key].compile(optimizer=keras.optimizers.SGD(lr=0.0001,
momentum=0.9),
**param_dict["compile_args"])
# Train again for the requested number of epochs:
print((
"\n\nFurther training (refining convolutional blocks, starting with"
"\n\tlayer {})...").format(freeze))
while initial_epoch < pass_num * param_dict["pass_epochs"]:
# Split into "epoch_batch_size"-epoch training batches
final_epoch = min(initial_epoch + epoch_batch_size,
pass_num * param_dict["pass_epochs"])
print("\nTraining for epochs {} to {}...".format(
initial_epoch + 1, final_epoch))
results_new = models[model_key].fit_generator(
generators["train"],
validation_data=generators["val"],
verbose=param_dict["run_verbosity"],
epochs=final_epoch,
steps_per_epoch=param_dict["steps_per_epoch"],
validation_steps=param_dict["validation_steps"],
use_multiprocessing=True,
initial_epoch=initial_epoch)
# Merge these new results with existing results for previous batches:
if results is not None:
# Merge the two results lists:
for key in results.history:
results.history[key].extend(results_new.history[key])
else:
results = results_new
initial_epoch = final_epoch
runsec = timer.toc()
# Create a new row for these results:
new_res = pd.Series()
assign_run_key(new_res, param_dict, run_started)
assign_opt_key(new_res, opt_key)
train_acc, val_acc = assign_results_history(new_res, model_key, runsec,
results)
# Add this to the results dataframe:
try:
fma_results = ut.load_obj(param_dict["fma_results_name"])
except:
fma_results = pd.DataFrame(dtype=float, columns=RESULTS_COLS)
fma_results = fma_results.append(new_res, ignore_index=True)
# And save:
ut.save_obj(fma_results, param_dict["fma_results_name"])
print(
("\n{} for {} to yield {:0.1%} training accuracy "
"and {:0.1%} validation accuracy in {:d} \nepochs "
"(x{} training phases).").format(timer.time_from_sec(runsec),
param_dict["model_names"][model_key],
train_acc, val_acc,
param_dict["pass_epochs"],
len(freeze_to) + 1))
# Save trained weights:
if save_weights:
weights_save_name = os.path.join(
"saved_weights",
"{}_{}_{}_{}.h5".format(
model_key,
formatted(opt_key), # 4 elements
formatted(run_key(param_dict, run_started)),
timer.datetimepath()))
ut.ensure_dir(weights_save_name)
models[model_key].save_weights(weights_save_name)
print("\n{} run complete at {}.".format(
param_dict["model_names"][model_key], timer.datetimestamp()))
# Tell keras to clear the the tensorflow backend session (helps with memory leaks;
# see: http://bit.ly/2xJZbAt )
print("Clearing keras's backend Tensorflow session...\n")
K.clear_session()
def get_encoder_parameters(MP, param_name="encoder_params_best"):
enc_parameters = utils.load_obj(MP["dirname"] + param_name)
return [param.get_value() for param in enc_parameters]
def run(dirname, save_weights, test_gratings, RF_comp, test_loglik,
train_loglik, plot_loglik, plot_train_loglik, save_test_latents,
n_data_samp, n_ais_step, n_prior_samp, n_hast_step, eps, n_ham_step,
use_prior, full, fast, seed, AIS_test):
np.random.seed(seed)
LOG = log.log(dirname + "/analysis_log.log")
MP = utils.load_obj(dirname + "model_params")
n_pca = int((MP["patch_size"]**2) * MP["pca_frac"])
#this is to handle legacy data files that didn't have the CNN keyword
if "CNN" not in MP.keys():
MP["CNN"] = False
if MP["CNN"]:
datsize = MP["patch_size"]**2
else:
datsize = n_pca
n_lat = int(n_pca * MP["overcomplete"])
MP["n_lat"] = n_lat
MP["n_pca"] = n_pca
MP["datsize"] = datsize
MP["dirname"] = dirname
for x in MP.keys():
print("{}\t{}".format(x, MP[x]))
train, test, var, PCA = dat.get_data(MP["patch_size"], n_pca,
MP["dataset"], MP["whiten"],
MP["CNN"])
LOG.log("Train Shape:\t{}".format(train.shape))
LOG.log("Test Shape:\t{}".format(test.shape))
LOG.log("Var Shape:\t{}".format(var.shape))
W = get_weights(MP)
try:
Wf = get_weights(MP, "decoder_params_final")
FINAL = True
except:
LOG.log("Final params not available")
FINAL = False
if save_weights or full or fast:
#W[0] is [144,NLAT]. teh PCA var is size n_pca. I want to take the PCA var, inverse transform it, and them normalize by it.
if MP["CNN"]:
w_norm = np.sqrt(np.reshape(PCA.explained_variance_, [1, -1]))
Wout = PCA.inverse_transform(
PCA.transform(np.transpose(W[0])) * w_norm)
else:
Wout = PCA.inverse_transform(np.transpose(W[0]))
LOG.log("Saving Weights")
np.savetxt(MP["dirname"] + "weights.csv", Wout)
if FINAL:
if MP["CNN"]:
w_norm = np.sqrt(np.reshape(PCA.explained_variance_, [1, -1]))
Wout = PCA.inverse_transform(
PCA.transform(np.transpose(Wf[0])) * w_norm)
#w_norm = PCA.inverse_transform(np.sqrt(np.reshape(PCA.explained_variance_,[1,-1])))
#Wout = np.transpose(Wf[0])*w_norm
else:
Wout = PCA.inverse_transform(np.transpose(W[0]))
np.savetxt(MP["dirname"] + "weights_final.csv", Wout)
if save_test_latents or full or fast:
LOG.log("Saving Latents")
mean, var, trans = get_latents(
test[:np.min([10 * n_data_samp, len(test)])],
MP,
W,
PCA,
SAVE=True)
trans1 = np.array([np.diag(x) for x in trans])
trans2 = trans[:, 0, :]
np.savetxt(MP['dirname'] + "test_means_best.csv", mean)
np.savetxt(
MP['dirname'] + "test_sample_best.csv",
np.array([
np.random.multivariate_normal(mean[v], var[v])
for v in range(len(var))
]))
np.savetxt(MP['dirname'] + "test_trans_diag_best.csv", trans1)
np.savetxt(MP['dirname'] + "test_trans_trans_best.csv", trans2)
if FINAL:
mean, var, trans = get_latents(
test[:np.min([10 * n_data_samp, len(test)])],
MP,
Wf,
PCA,
SAVE=True)
trans1 = np.array([np.diag(x) for x in trans])
trans2 = trans[:, 0, :]
np.savetxt(MP['dirname'] + "test_means_final.csv", mean)
np.savetxt(MP['dirname'] + "test_trans_diag_final.csv", trans1)
np.savetxt(MP['dirname'] + "test_trans_trans_final.csv", trans2)
if MP["CNN"]:
norm = np.sqrt(np.reshape(PCA.explained_variance_, [1, -1]))
out = PCA.inverse_transform(
PCA.transform(
test[:np.min([10 *
n_data_samp, len(test)])]) * w_norm)
else:
out = PCA.inverse_transform(
test[:np.min([10 * n_data_samp, len(test)])])
np.savetxt(
MP["dirname"] + "test_images.csv", out
) #PCA.inverse_transform(test[:np.min([n_data_samp,len(test)])]))
if test_gratings or full or fast:
LOG.log("Processing Gratings")
mean, lab, grats = grating_test(MP, PCA)
np.savetxt(MP["dirname"] + "test_grating.csv", mean)
np.savetxt(MP["dirname"] + "test_grating_labels.csv", lab)
np.savetxt(MP["dirname"] + "test_grating_images.csv", grats)
if RF_comp or full or fast:
LOG.log("Calculating RFs")
for scale in [.4, .5, .6]:
RFs = RF_test(MP, PCA, scale)
for k in range(len(RFs)):
np.savetxt(
MP["dirname"] +
"receptive_fields_{}_{}.csv".format(k, scale), RFs[k])
if test_loglik or full:
LOG.log("Calculating Likelihoods")
plot_loglikelihood(test[:np.min([n_data_samp, len(test)])],
MP,
"test_final_loglik.csv",
indices=["best"],
n_ais_step=n_ais_step,
n_prior_samp=n_prior_samp,
n_hast_step=n_hast_step,
eps=eps,
n_ham_step=n_ham_step,
use_prior=use_prior,
LOG=LOG)
if train_loglik or full:
LOG.log("Calculating Likelihoods")
plot_loglikelihood(train[:np.min([n_data_samp, len(train)])],
MP,
"train_final_loglik.csv",
indices=["best"],
n_ais_step=n_ais_step,
n_prior_samp=n_prior_samp,
n_hast_step=n_hast_step,
eps=eps,
n_ham_step=n_ham_step,
use_prior=use_prior,
LOG=LOG)
if plot_loglik or full:
LOG.log("Plotting Likelihoods")
plot_loglikelihood(test[:np.min([n_data_samp, len(test)])],
MP,
"test_plot_loglik.csv",
n_ais_step=n_ais_step,
n_prior_samp=n_prior_samp,
n_hast_step=n_hast_step,
eps=eps,
n_ham_step=n_ham_step,
use_prior=use_prior,
LOG=LOG)
if plot_train_loglik or full:
LOG.log("Plotting Likelihoods")
plot_loglikelihood(train[:np.min([n_data_samp, len(test)])],
MP,
"train_plot_loglik.csv",
n_ais_step=n_ais_step,
n_prior_samp=n_prior_samp,
n_hast_step=n_hast_step,
eps=eps,
n_ham_step=n_ham_step,
use_prior=use_prior,
LOG=LOG)
if AIS_test:
test_loglikelihood(test[:2], MP, "best", n_ais_step, n_prior_samp,
n_hast_step, eps, n_ham_step, use_prior, LOG)
def get_weights(MP, param_name="decoder_params_best"):
W = utils.load_obj(MP["dirname"] + param_name)
return W
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# ----------------------------------------------------------------------------------------
# Create model(s) and send to device(s)
# ----------------------------------------------------------------------------------------
net = model.ResUNet(3, False).float()
net.load_state_dict(torch.load('ResUNet.pt'))
if args.distributed:
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(
(args.workers + ngpus_per_node - 1) / ngpus_per_node)
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(
net, device_ids=[args.gpu])
else:
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(net)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
net.cuda(args.gpu)
else:
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(net)
# ----------------------------------------------------------------------------------------
# Define dataset path and data splits
# ----------------------------------------------------------------------------------------
#Input_Data = scipy.io.loadmat("\Path\To\Inputs.mat")
#Output_Data = scipy.io.loadmat("\Path\To\Outputs.mat")
#Input = Input_Data['data']
#Output = Output_Data['data']
Input = utilities.load_obj(
f'{args.path_to_data}/inputs') #Input_Data['Inputs']
Output = utilities.load_obj(
f'{args.path_to_data}/outputs') # Output_Data['Outputs']
# ----------------------------------------------------------------------------------------
# Create datasets (with augmentation) and dataloaders
# ----------------------------------------------------------------------------------------
Raman_Dataset_Test = dataset.RamanDataset(Input,
Output,
batch_size=args.batch_size,
spectrum_len=args.spectrum_len)
test_loader = DataLoader(Raman_Dataset_Test,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True)
# ----------------------------------------------------------------------------------------
# Evaluate
# ----------------------------------------------------------------------------------------
MSE_NN, MSE_SG = evaluate(test_loader, net, args)
args.PATH_IMAGES = PATH_IMAGES
args.DATA_NAME = DATA_NAME
TENSOR_FROZEN_MODEL_PATH = './models/frozen_model.pb'
graph = load_graph(TENSOR_FROZEN_MODEL_PATH)
sess = tf.Session(graph=graph,
config=tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.60)))
output = sess.graph.get_tensor_by_name('model/embeddings:0')
input = sess.graph.get_tensor_by_name('model/input:0')
phase_train_placeholder = sess.graph.get_tensor_by_name('model/phase_train:0')
data_pkl = load_obj(DATA_NAME)
@app.route('/', methods=['POST'])
def post():
data = request.get_json()
parent_path = os.path.join(args.PATH_IMAGES, data['parent_path'])
# print("parent path: ", parent_path)
for child in data['childrent']:
path_child, x, y, w, h = child
embedding = get_embedding(path_child, input, phase_train_placeholder,
output, sess)
person_id = find_min(embedding, data_pkl)
post_to_main_server(person_id, parent_path, (x, y, w, h))
return 'OK!'
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# ----------------------------------------------------------------------------------------
# Create model(s) and send to device(s)
# ----------------------------------------------------------------------------------------
net = model.ResUNet(3, args.batch_norm).float()
if args.distributed:
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(
(args.workers + ngpus_per_node - 1) / ngpus_per_node)
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(
net, device_ids=[args.gpu])
else:
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(net)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
net.cuda(args.gpu)
else:
net.cuda(args.gpu)
net = torch.nn.parallel.DistributedDataParallel(net)
# ----------------------------------------------------------------------------------------
# Define dataset path and data splits
# ----------------------------------------------------------------------------------------
#Input_Data = #scipy.io.loadmat("\Path\To\Inputs.mat")
#Output_Data = #scipy.io.loadmat("\Path\To\Outputs.mat")
Input = utilities.load_obj(
f'{args.path_to_data}/inputs') #Input_Data['Inputs']
Output = utilities.load_obj(
f'{args.path_to_data}/outputs') # Output_Data['Outputs']
spectra_num = len(Input)
train_split = round(0.9 * spectra_num)
val_split = round(0.1 * spectra_num)
input_train = Input[:train_split]
input_val = Input[train_split:train_split + val_split]
output_train = Output[:train_split]
output_val = Output[train_split:train_split + val_split]
# ----------------------------------------------------------------------------------------
# Create datasets (with augmentation) and dataloaders
# ----------------------------------------------------------------------------------------
Raman_Dataset_Train = dataset.RamanDataset(input_train,
output_train,
batch_size=args.batch_size,
spectrum_len=args.spectrum_len,
spectrum_shift=0.1,
spectrum_window=False,
horizontal_flip=False,
mixup=True)
Raman_Dataset_Val = dataset.RamanDataset(input_val,
output_val,
batch_size=args.batch_size,
spectrum_len=args.spectrum_len)
train_loader = DataLoader(Raman_Dataset_Train,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True)
val_loader = DataLoader(Raman_Dataset_Val,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True)
# ----------------------------------------------------------------------------------------
# Define criterion(s), optimizer(s), and scheduler(s)
# ----------------------------------------------------------------------------------------
criterion = nn.L1Loss().cuda(args.gpu)
criterion_MSE = nn.MSELoss().cuda(args.gpu)
if args.optimizer == "sgd":
optimizer = optim.SGD(net.parameters(), lr=args.lr)
elif args.optimizer == "adamW":
optimizer = optim.AdamW(net.parameters(), lr=args.lr)
else: # Adam
optimizer = optim.Adam(net.parameters(), lr=args.lr)
if args.scheduler == "decay-lr":
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=50,
gamma=0.2)
elif args.scheduler == "multiplicative-lr":
lmbda = lambda epoch: 0.985
scheduler = optim.lr_scheduler.MultiplicativeLR(optimizer,
lr_lambda=lmbda)
elif args.scheduler == "cyclic-lr":
scheduler = optim.lr_scheduler.CyclicLR(optimizer,
base_lr=args.base_lr,
max_lr=args.lr,
mode='triangular2',
cycle_momentum=False)
elif args.scheduler == "one-cycle-lr":
scheduler = optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=args.lr,
steps_per_epoch=len(train_loader),
epochs=args.epochs,
cycle_momentum=False)
else: # constant-lr
scheduler = None
print('Started Training')
print('Training Details:')
#print('Network: {}'.format(args.network))
print('Epochs: {}'.format(args.epochs))
print('Batch Size: {}'.format(args.batch_size))
print('Optimizer: {}'.format(args.optimizer))
print('Scheduler: {}'.format(args.scheduler))
print('Learning Rate: {}'.format(args.lr))
print('Spectrum Length: {}'.format(args.spectrum_len))
DATE = datetime.datetime.now().strftime("%Y_%m_%d")
log_dir = "runs/{}_{}_{}".format(DATE, args.optimizer,
args.scheduler) #, args.network)
models_dir = "{}_{}_{}.pt".format(DATE, args.optimizer,
args.scheduler) #, args.network)
writer = SummaryWriter(log_dir=log_dir)
for epoch in range(args.epochs):
train_loss = train(train_loader, net, optimizer, scheduler, criterion,
criterion_MSE, epoch, args)
val_loss = validate(val_loader, net, criterion_MSE, args)
if args.scheduler == "decay-lr" or args.scheduler == "multiplicative-lr":
scheduler.step()
writer.add_scalar('Loss/train', train_loss, epoch)
writer.add_scalar('Loss/val', val_loss, epoch)
torch.save(net.state_dict(), models_dir)
print('Finished Training')
def run(dirname):
LOG = log.log(dirname + "/weight_log.log")
MP = utils.load_obj(dirname + "model_params")
n_pca = int((MP["patch_size"]**2) * MP["pca_frac"])
#this is to handle legacy data files that didn't have the CNN keyword
if "CNN" not in MP.keys():
MP["CNN"] = False
if MP["CNN"]:
datsize = MP["patch_size"]**2
else:
datsize = n_pca
n_lat = int(n_pca * MP["overcomplete"])
MP["n_lat"] = n_lat
MP["n_pca"] = n_pca
MP["datsize"] = datsize
MP["dirname"] = dirname
for x in MP.keys():
print("{}\t{}".format(x, MP[x]))
train, test, var, PCA = dat.get_data(MP["patch_size"], n_pca,
MP["dataset"], MP["whiten"],
MP["CNN"])
LOG.log("Train Shape:\t{}".format(train.shape))
LOG.log("Test Shape:\t{}".format(test.shape))
LOG.log("Var Shape:\t{}".format(var.shape))
W = get_weights(MP)
try:
Wf = get_weights(MP, "decoder_params_final")
FINAL = True
except:
LOG.log("Final params not available")
FINAL = False
LOG.log(np.std(test))
sp1 = np.random.randn(test.shape[0], n_lat) * MP["s1"]
sp2 = np.random.randn(test.shape[0], n_lat) * MP["s2"]
S = MP["S"]
LOG.log("sp1 {}".format(np.std(sp1)))
LOG.log("sp2 {}".format(np.std(sp2)))
LOG.log("Wsp1 {}".format(np.std(np.tensordot(sp1, W[0], axes=[1, 1]))))
LOG.log("Wsp2 {}".format(np.std(np.tensordot(sp2, W[0], axes=[1, 1]))))
LOG.log("SW {}".format(S * np.std(np.tensordot(sp2, W[0], axes=[1, 1])) +
(1. - S) *
np.std(np.tensordot(sp1, W[0], axes=[1, 1]))))
A = get_file(MP["dirname"] + "/test_means_best.csv")
LOG.log("RV {}".format(np.std(np.tensordot(W[0], A, axes=[1, 1]))))
LOG.log("DV {}".format(np.std(var)))