diff --git a/video_prediction_savp/HPC_scripts/train_movingmnist.sh b/video_prediction_savp/HPC_scripts/train_movingmnist.sh
index 9f0ea378196d1991a3f5146fb1df1e80cd6fd6b7..68b107e2e984eeb797ee4df8f6535486e1366c6a 100755
--- a/video_prediction_savp/HPC_scripts/train_movingmnist.sh
+++ b/video_prediction_savp/HPC_scripts/train_movingmnist.sh
@@ -6,9 +6,9 @@
#SBATCH --cpus-per-task=1
#SBATCH --output=train_era5-out.%j
#SBATCH --error=train_era5-err.%j
-#SBATCH --time=00:20:00
-#SBATCH --gres=gpu:2
-#SBATCH --partition=develgpus
+#SBATCH --time=23:20:00
+#SBATCH --gres=gpu:1
+#SBATCH --partition=gpus
#SBATCH --mail-type=ALL
#SBATCH --mail-user=b.gong@fz-juelich.de
##jutil env activate -p cjjsc42
@@ -31,14 +31,12 @@ if [ -z ${VIRTUAL_ENV} ]; then
fi
-
-
# declare directory-variables which will be modified appropriately during Preprocessing (invoked by mpi_split_data_multi_years.py)
source_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/preprocessedData/moving_mnist
-destination_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/models/moving_mnist
+destination_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/models/moving_mnist_test
-# for choosing the model, convLSTM,savp, mcnet,vae
+# for choosing the model, convLSTM,savp, mcnet,vae,convLSTM_Loliver
model=convLSTM
model_hparams=../hparams/era5/${model}/model_hparams.json
diff --git a/video_prediction_savp/scripts/generate_transfer_learning_finetune.py b/video_prediction_savp/scripts/generate_transfer_learning_finetune.py
index 0e250b47df28d115c8cdfc77fc708eab5e094ce6..7b0c462c3a2f7e17956c00906cb39582bb2e3db7 100644
--- a/video_prediction_savp/scripts/generate_transfer_learning_finetune.py
+++ b/video_prediction_savp/scripts/generate_transfer_learning_finetune.py
@@ -202,11 +202,6 @@ def save_to_netcdf_per_sequence(output_dir,input_images_,gen_images_,lons,lats,t
ts_len = len(ts)
ts_input = ts[:context_frames]
ts_forecast = ts[context_frames:]
- #print("context_frame:",context_frames)
- #print("future_frame",future_length)
- #print("length of ts input:",len(ts_input))
-
- print("input_images_ shape in netcdf,",input_images_.shape)
gen_images_ = np.array(gen_images_)
output_file = os.path.join(output_dir,fl_name)
@@ -289,18 +284,19 @@ def save_to_netcdf_per_sequence(output_dir,input_images_,gen_images_,lons,lats,t
#Temperature:
t2 = nc_file.createVariable("/forecast/{}/T2".format(model_name),"f4",("time_forecast","lat","lon"), zlib = True)
t2.units = 'K'
- t2[:,:,:] = gen_images_[context_frames:,:,:,0]
+ print ("gen_images_ 20200822:",np.array(gen_images_).shape)
+ t2[:,:,:] = gen_images_[context_frames-1:,:,:,0]
print("NetCDF created")
#mean sea level pressure
msl = nc_file.createVariable("/forecast/{}/MSL".format(model_name),"f4",("time_forecast","lat","lon"), zlib = True)
msl.units = 'Pa'
- msl[:,:,:] = gen_images_[context_frames:,:,:,1]
+ msl[:,:,:] = gen_images_[context_frames-1:,:,:,1]
#Geopotential at 500
gph500 = nc_file.createVariable("/forecast/{}/GPH500".format(model_name),"f4",("time_forecast","lat","lon"), zlib = True)
gph500.units = 'm'
- gph500[:,:,:] = gen_images_[context_frames:,:,:,2]
+ gph500[:,:,:] = gen_images_[context_frames-1:,:,:,2]
print("{} created".format(output_file))
@@ -451,7 +447,8 @@ def main():
#Get prediction values
feed_dict = {input_ph: input_results[name] for name, input_ph in input_phs.items()}
gen_images = sess.run(model.outputs['gen_images'], feed_dict = feed_dict)#return [batchsize,seq_len,lat,lon,channel]
-
+ assert gen_images.shape[1] = sequence_length-1 #The generate images seq_len should be sequence_len -1, since the last one is not used for comparing with groud truth
+ print("gen_images 20200822:",np.array(gen_images).shape)
#Loop in batch size
for i in range(args.batch_size):
@@ -459,26 +456,26 @@ def main():
input_images_,t_start = get_one_seq_and_time(input_images,t_starts,i)
#generate time stamps for sequences
ts = generate_seq_timestamps(t_start,len_seq=sequence_length)
-
+
#Renormalized data for inputs
stat_fl = os.path.join(args.input_dir,"pickle/statistics.json")
input_images_denorm = denorm_images_all_channels(stat_fl,input_images_,["T2","MSL","gph500"])
- print("input_images_denorm",input_images_denorm[0][0])
+ print("input_images_denorm shape",np.array(input_images_denorm).shape)
#Renormalized data for inputs
gen_images_ = gen_images[i]
gen_images_denorm = denorm_images_all_channels(stat_fl,gen_images_,["T2","MSL","gph500"])
- print("gene_images_denorm:",gen_images_denorm[0][0])
+ print("gene_images_denorm shape",np.array(gen_images_denorm).shape)
#Save input to netCDF file
init_date_str = ts[0].strftime("%Y%m%d%H")
save_to_netcdf_per_sequence(args.results_dir,input_images_denorm,gen_images_denorm,lons,lats,ts,context_frames,future_length,args.model,fl_name="vfp_{}.nc".format(init_date_str))
#Generate images inputs
- plot_seq_imgs(imgs=input_images_denorm[:context_frames-1,:,:,0],lats=lats,lons=lons,ts=ts[:context_frames-1],label="Ground Truth",output_png_dir=args.results_dir)
+ plot_seq_imgs(imgs=input_images_denorm[context_frames+1:,:,:,0],lats=lats,lons=lons,ts=ts[context_frames+1:],label="Ground Truth",output_png_dir=args.results_dir)
#Generate forecast images
- plot_seq_imgs(imgs=gen_images_denorm[context_frames:,:,:,0],lats=lats,lons=lons,ts=ts[context_frames:],label="Forecast by Model " + args.model,output_png_dir=args.results_dir)
+ plot_seq_imgs(imgs=gen_images_denorm[context_frames:,:,:,0],lats=lats,lons=lons,ts=ts[context_frames+1:],label="Forecast by Model " + args.model,output_png_dir=args.results_dir)
#TODO: Scaret plot persistence image
#implment get_persistence() function
diff --git a/video_prediction_savp/video_prediction/datasets/kth_dataset.py b/video_prediction_savp/video_prediction/datasets/kth_dataset.py
index 40fb6bf57b8219fc7e75c3759df9e6b38fffeb30..e1e11d51968e706868fd89f26faa25d1999d3a9b 100644
--- a/video_prediction_savp/video_prediction/datasets/kth_dataset.py
+++ b/video_prediction_savp/video_prediction/datasets/kth_dataset.py
@@ -136,11 +136,11 @@ def read_frames_and_save_tf_records(output_dir, video_dirs, image_size, sequence
def main():
parser = argparse.ArgumentParser()
- parser.add_argument("--input_dir", type=str, help="directory containing the processed directories "
+ parser.add_argument("input_dir", type=str, help="directory containing the processed directories "
"boxing, handclapping, handwaving, "
"jogging, running, walking")
- parser.add_argument("--output_dir", type=str)
- parser.add_argument("--image_size", type=int)
+ parser.add_argument("output_dir", type=str)
+ parser.add_argument("image_size", type=int)
args = parser.parse_args()
partition_names = ['train', 'val', 'test']
diff --git a/video_prediction_savp/video_prediction/datasets/moving_mnist.py b/video_prediction_savp/video_prediction/datasets/moving_mnist.py
index 8966e1af1fbd80e35f0d4b9a4575a05d02cfe144..12a517ac9540dd443866d47ea689803abe9e9a4d 100644
--- a/video_prediction_savp/video_prediction/datasets/moving_mnist.py
+++ b/video_prediction_savp/video_prediction/datasets/moving_mnist.py
@@ -207,7 +207,6 @@ def plot_seq_imgs(imgs,output_png_dir,idx,label="Ground Truth"):
-
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input_dir", type=str, help="directory containing the processed directories ""boxing, handclapping, handwaving, ""jogging, running, walking")
@@ -221,12 +220,15 @@ def main():
height = data.shape[2]
width = data.shape[3]
num_samples = data.shape[1]
-
+ max_npz = np.max(data)
+ min_npz = np.min(data)
+ print("max_npz,",max_npz)
+ print("min_npz",min_npz)
#Todo need to discuss how to split the data, since we have totally 10000 samples, the origin paper convLSTM used 10000 as training, 2000 as validation and 3000 for testing
dat_train = data[:,:6000,:,:]
dat_val = data[:,6000:7000,:,:]
dat_test = data[:,7000:,:]
- plot_seq_imgs(dat_test[10:,0,:,:],output_png_dir="/p/project/deepacf/deeprain/video_prediction_shared_folder/results/moving_mnist/convLSTM",idx=1,label="Ground Truth from npz")
+ #plot_seq_imgs(dat_test[10:,0,:,:],output_png_dir="/p/project/deepacf/deeprain/video_prediction_shared_folder/results/moving_mnist/convLSTM",idx=1,label="Ground Truth from npz")
#save train
#read_frames_and_save_tf_records(os.path.join(args.output_dir,"train"),dat_train, seq_length=20, sequences_per_file=40, height=height, width=width)
#save val
diff --git a/video_prediction_savp/video_prediction/layers/layer_def.py b/video_prediction_savp/video_prediction/layers/layer_def.py
index a59643c7a6d69141134ec01c9b147c4798bfed8e..738e139df0e155ca294fe43edd03a2d79fc1f532 100644
--- a/video_prediction_savp/video_prediction/layers/layer_def.py
+++ b/video_prediction_savp/video_prediction/layers/layer_def.py
@@ -3,8 +3,8 @@
import tensorflow as tf
import numpy as np
-
weight_decay = 0.0005
+
def _activation_summary(x):
"""Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
@@ -157,4 +157,4 @@ def bn_layers(inputs,idx,is_training=True,epsilon=1e-3,decay=0.99,reuse=None):
def bn_layers_wrapper(inputs, is_training):
pass
-
\ No newline at end of file
+
diff --git a/video_prediction_savp/video_prediction/models/__init__.py b/video_prediction_savp/video_prediction/models/__init__.py
index 6d7323f3750949b0ddb411d4a98934928537bc53..b71769a9d8cc523e4f108fc222fc2ed0284019f7 100644
--- a/video_prediction_savp/video_prediction/models/__init__.py
+++ b/video_prediction_savp/video_prediction/models/__init__.py
@@ -21,7 +21,7 @@ def get_model_class(model):
'vae': 'VanillaVAEVideoPredictionModel',
'convLSTM': 'VanillaConvLstmVideoPredictionModel',
'mcnet': 'McNetVideoPredictionModel',
-
+ 'convLSTM_Loliver': "ConvLstmLoliverVideoPredictionModel"
}
model_class = model_mappings.get(model, model)
model_class = globals().get(model_class)
diff --git a/video_prediction_savp/video_prediction/models/base_model.py b/video_prediction_savp/video_prediction/models/base_model.py
index 846621d8ca1e235c39618951be86fe184a2d974d..df479968325946a9d61896d498428d65692c1848 100644
--- a/video_prediction_savp/video_prediction/models/base_model.py
+++ b/video_prediction_savp/video_prediction/models/base_model.py
@@ -487,6 +487,7 @@ class VideoPredictionModel(BaseVideoPredictionModel):
# skip_vars = {" discriminator_encoder/video_sn_fc4/dense/bias"}
if self.num_gpus <= 1: # cpu or 1 gpu
+ print("self.inputs:>20200822",self.inputs)
outputs_tuple, losses_tuple, loss_tuple, metrics_tuple = self.tower_fn(self.inputs)
self.outputs, self.eval_outputs = outputs_tuple
self.d_losses, self.g_losses, g_losses_post = losses_tuple
diff --git a/video_prediction_savp/video_prediction/models/savp_model.py b/video_prediction_savp/video_prediction/models/savp_model.py
index c510d050c89908d0e06fe0f1a66e355e61c90530..039533864f34d1608c5a10d4a664d40ce73594a7 100644
--- a/video_prediction_savp/video_prediction/models/savp_model.py
+++ b/video_prediction_savp/video_prediction/models/savp_model.py
@@ -689,6 +689,10 @@ class SAVPCell(tf.nn.rnn_cell.RNNCell):
def generator_given_z_fn(inputs, mode, hparams):
# all the inputs needs to have the same length for unrolling the rnn
print("inputs.items",inputs.items())
+ #20200822 bing
+ inputs ={"images":inputs["images"]}
+ print("inputs 20200822:",inputs)
+ #20200822
inputs = {name: tf_utils.maybe_pad_or_slice(input, hparams.sequence_length - 1)
for name, input in inputs.items()}
cell = SAVPCell(inputs, mode, hparams)
diff --git a/video_prediction_savp/video_prediction/models/vanilla_convLSTM_model.py b/video_prediction_savp/video_prediction/models/vanilla_convLSTM_model.py
index ee0b0c0b8a4b4fd20f2e73458ea622cece174fa6..f64ce663858f6d95b8fafe42f32c78381e436024 100644
--- a/video_prediction_savp/video_prediction/models/vanilla_convLSTM_model.py
+++ b/video_prediction_savp/video_prediction/models/vanilla_convLSTM_model.py
@@ -89,9 +89,7 @@ class VanillaConvLstmVideoPredictionModel(BaseVideoPredictionModel):
def convLSTM_cell(inputs, hidden):
channels = inputs.get_shape()[-1]
conv1 = ld.conv_layer(inputs, 3, 2, 8, "encode_1", activate = "leaky_relu")
-
conv2 = ld.conv_layer(conv1, 3, 1, 8, "encode_2", activate = "leaky_relu")
-
conv3 = ld.conv_layer(conv2, 3, 2, 8, "encode_3", activate = "leaky_relu")
y_0 = conv3