diff --git a/scripts/generate_transfer_learning_finetune.py b/scripts/generate_transfer_learning_finetune.py
index 80470df25b5db6854c15d343bf2e62c596b40cb5..c990ba574aba558dbd0735f85f02f82f2118ca08 100644
--- a/scripts/generate_transfer_learning_finetune.py
+++ b/scripts/generate_transfer_learning_finetune.py
@@ -33,7 +33,7 @@ from matplotlib.colors import LinearSegmentedColormap
from skimage.metrics import structural_similarity as ssim
import pickle
-with open("./splits_size_64_64_1/geo_info.json","r") as json_file:
+with open("geo_info.json","r") as json_file:
geo = json.load(json_file)
lat = [round(i,2) for i in geo["lat"]]
lon = [round(i,2) for i in geo["lon"]]
@@ -196,190 +196,190 @@ def main():
gen_images_all = []
input_images_all = []
- # while True:
- # print("Sample id", sample_ind)
- # gen_images_stochastic = []
- # if args.num_samples and sample_ind >= args.num_samples:
- # break
- # try:
- # input_results = sess.run(inputs)
- # input_images = input_results["images"]
- # input_images_all.extend(input_images)
- # with open(os.path.join(args.output_png_dir, "input_images_all"), "wb") as input_files:
- # pickle.dump(list(input_images_all), input_files)
- #
- # except tf.errors.OutOfRangeError:
- # break
- #
- # feed_dict = {input_ph: input_results[name] for name, input_ph in input_phs.items()}
- # for stochastic_sample_ind in range(args.num_stochastic_samples):
- # gen_images = sess.run(model.outputs['gen_images'], feed_dict = feed_dict)
- # gen_images_stochastic.append(gen_images)
- # print("Stochastic_sample,", stochastic_sample_ind)
- # for i in range(args.batch_size):
- # print("batch", i)
- # #colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
- # cmap_name = 'my_list'
- # if sample_ind < 20 and i == 1:
- # name = 'Stochastic_id_' + str(stochastic_sample_ind) + 'Batch_id_' + str(
- # sample_ind) + " + Sample_" + str(i)
- # gen_images_ = np.array(list(input_images[i,:context_frames]) + list(gen_images[i,-future_length:, :]))
- # #gen_images_ = gen_images[i, :]
- # input_images_ = input_images[i, :]
- # input_gen_diff = (input_images_[:, :, :,0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922) - (gen_images_[:, :, :, 0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922)
- #
- # gen_mse_avg_ = [np.mean(input_gen_diff[frame, :, :] ** 2) for frame in
- # range(sequence_length)] # return the list with 10 (sequence) mse
-
- # fig = plt.figure(figsize=(18,6))
- # gs = gridspec.GridSpec(1, 10)
- # gs.update(wspace = 0., hspace = 0.)
- # ts = [0,5,9,10,12,14,16,18,19]
- # xlables = [round(i,2) for i in list(np.linspace(np.min(lon),np.max(lon),5))]
- # ylabels = [round(i,2) for i in list(np.linspace(np.max(lat),np.min(lat),5))]
- #
- # for t in range(len(ts)):
- # #if t==0 : ax1=plt.subplot(gs[t])
- # ax1 = plt.subplot(gs[t])
- # input_image = input_images_[ts[t], :, :, 0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922
- # plt.imshow(input_image, cmap = 'jet', vmin=270, vmax=300)
- # ax1.title.set_text("t = " + str(ts[t]+1))
- # plt.setp([ax1], xticks = [], xticklabels = [], yticks = [], yticklabels = [])
- #
- # if t == 0:
- # plt.setp([ax1], xticks = list(np.linspace(0, 64, 3)), xticklabels = xlables, yticks = list(np.linspace(0, 64, 3)), yticklabels = ylabels)
- # plt.ylabel("Ground Truth", fontsize=10)
- # plt.savefig(os.path.join(args.output_png_dir, "Ground_Truth_Sample_" + str(name) + ".jpg"))
- # plt.clf()
- #
- # fig = plt.figure(figsize=(12,6))
- # gs = gridspec.GridSpec(1, 10)
- # gs.update(wspace = 0., hspace = 0.)
- # ts = [10,12,14,16,18,19]
- # for t in range(len(ts)):
- # #if t==0 : ax1=plt.subplot(gs[t])
- # ax1 = plt.subplot(gs[t])
- # gen_image = gen_images_[ts[t], :, :, 0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922
- # plt.imshow(gen_image, cmap = 'jet', vmin=270, vmax=300)
- # ax1.title.set_text("t = " + str(ts[t]+1))
- # plt.setp([ax1], xticks = [], xticklabels = [], yticks = [], yticklabels = [])
- #
- # plt.savefig(os.path.join(args.output_png_dir, "Predicted_Sample_" + str(name) + ".jpg"))
- # plt.clf()
- #
- # fig = plt.figure()
- # gs = gridspec.GridSpec(4,6)
- # gs.update(wspace = 0.7,hspace=0.8)
- # ax1 = plt.subplot(gs[0:2,0:3])
- # ax2 = plt.subplot(gs[0:2,3:],sharey=ax1)
- # ax3 = plt.subplot(gs[2:4,0:3])
- # ax4 = plt.subplot(gs[2:4,3:])
- # xlables = [round(i,2) for i in list(np.linspace(np.min(lon),np.max(lon),5))]
- # ylabels = [round(i,2) for i in list(np.linspace(np.max(lat),np.min(lat),5))]
- # plt.setp([ax1,ax2,ax3],xticks=list(np.linspace(0,64,5)), xticklabels=xlables ,yticks=list(np.linspace(0,64,5)),yticklabels=ylabels)
- # ax1.title.set_text("(a) Ground Truth")
- # ax2.title.set_text("(b) SAVP")
- # ax3.title.set_text("(c) Diff.")
- # ax4.title.set_text("(d) MSE")
- #
- # ax1.xaxis.set_tick_params(labelsize=7)
- # ax1.yaxis.set_tick_params(labelsize = 7)
- # ax2.xaxis.set_tick_params(labelsize=7)
- # ax2.yaxis.set_tick_params(labelsize = 7)
- # ax3.xaxis.set_tick_params(labelsize=7)
- # ax3.yaxis.set_tick_params(labelsize = 7)
- #
- # init_images = np.zeros((input_images_.shape[1], input_images_.shape[2]))
- # print("inti images shape", init_images.shape)
- # xdata, ydata = [], []
- # #plot1 = ax1.imshow(init_images, cmap='jet', vmin =0, vmax = 1)
- # #plot2 = ax2.imshow(init_images, cmap='jet', vmin =0, vmax = 1)
- # plot1 = ax1.imshow(init_images, cmap='jet', vmin = 270, vmax = 300)
- # plot2 = ax2.imshow(init_images, cmap='jet', vmin = 270, vmax = 300)
- # #x = np.linspace(0, 64, 64)
- # #y = np.linspace(0, 64, 64)
- # #plot1 = ax1.contourf(x,y,init_images, cmap='jet', vmin = np.min(input_images), vmax = np.max(input_images))
- # #plot2 = ax2.contourf(x,y,init_images, cmap='jet', vmin = np.min(input_images), vmax = np.max(input_images))
- # fig.colorbar(plot1, ax=ax1).ax.tick_params(labelsize=7)
- # fig.colorbar(plot2, ax=ax2).ax.tick_params(labelsize=7)
- #
- # cm = LinearSegmentedColormap.from_list(
- # cmap_name, "bwr", N = 5)
- #
- # plot3 = ax3.imshow(init_images, vmin=-20, vmax=20, cmap=cm)#cmap = 'PuBu_r',
- # #plot3 = ax3.imshow(init_images, vmin = -1, vmax = 1, cmap = cm) # cmap = 'PuBu_r',
- # plot4, = ax4.plot([], [], color = "r")
- # ax4.set_xlim(0, future_length-1)
- # ax4.set_ylim(0, 20)
- # #ax4.set_ylim(0, 0.5)
- # ax4.set_xlabel("Frames", fontsize=10)
- # #ax4.set_ylabel("MSE", fontsize=10)
- # ax4.xaxis.set_tick_params(labelsize=7)
- # ax4.yaxis.set_tick_params(labelsize=7)
- #
- #
- # plots = [plot1, plot2, plot3, plot4]
- #
- # #fig.colorbar(plots[1], ax = [ax1, ax2])
- #
- # fig.colorbar(plots[2], ax=ax3).ax.tick_params(labelsize=7)
- # #fig.colorbar(plot1[0], ax=ax1).ax.tick_params(labelsize=7)
- # #fig.colorbar(plot2[1], ax=ax2).ax.tick_params(labelsize=7)
- #
- # def animation_sample(t):
- # input_image = input_images_[t, :, :, 0]* (321.46630859375-235.2141571044922) + 235.2141571044922
- # gen_image = gen_images_[t, :, :, 0]* (321.46630859375-235.2141571044922) + 235.2141571044922
- # diff_image = input_gen_diff[t,:,:]
- # # p = sns.lineplot(x=x,y=data,color="b")
- # # p.tick_params(labelsize=17)
- # # plt.setp(p.lines, linewidth=6)
- # plots[0].set_data(input_image)
- # plots[1].set_data(gen_image)
- # #plots[0] = ax1.contourf(x, y, input_image, cmap = 'jet', vmin = np.min(input_images),vmax = np.max(input_images))
- # #plots[1] = ax2.contourf(x, y, gen_image, cmap = 'jet', vmin = np.min(input_images),vmax = np.max(input_images))
- # plots[2].set_data(diff_image)
- #
- # if t >= future_length:
- # #data = gen_mse_avg_[:t + 1]
- # # x = list(range(len(gen_mse_avg_)))[:t+1]
- # xdata.append(t-future_length)
- # print("xdata", xdata)
- # ydata.append(gen_mse_avg_[t])
- # print("ydata", ydata)
- # plots[3].set_data(xdata, ydata)
- # fig.suptitle("Predicted Frame " + str(t-future_length))
- # else:
- # #plots[3].set_data(xdata, ydata)
- # fig.suptitle("Context Frame " + str(t))
- # return plots
- #
- # ani = animation.FuncAnimation(fig, animation_sample, frames=len(gen_mse_avg_), interval = 1000,
- # repeat_delay=2000)
- # ani.save(os.path.join(args.output_png_dir, "Sample_" + str(name) + ".mp4"))
- #
- # else:
- # pass
-
-
- #
- # if sample_ind == 0:
- # gen_images_all = gen_images_stochastic
- # else:
- # gen_images_all = np.concatenate((np.array(gen_images_all), np.array(gen_images_stochastic)), axis=1)
- #
- # if args.num_stochastic_samples == 1:
- # with open(os.path.join(args.output_png_dir, "gen_images_all"), "wb") as gen_files:
- # pickle.dump(list(gen_images_all[0]), gen_files)
- # else:
- # with open(os.path.join(args.output_png_dir, "gen_images_sample_id_" + str(sample_ind)),"wb") as gen_files:
- # pickle.dump(list(gen_images_stochastic), gen_files)
- # with open(os.path.join(args.output_png_dir, "gen_images_all_stochastic"), "wb") as gen_files:
- # pickle.dump(list(gen_images_all), gen_files)
- #
- #
- #
- #
- # sample_ind += args.batch_size
+ while True:
+ print("Sample id", sample_ind)
+ gen_images_stochastic = []
+ if args.num_samples and sample_ind >= args.num_samples:
+ break
+ try:
+ input_results = sess.run(inputs)
+ input_images = input_results["images"]
+ input_images_all.extend(input_images)
+ with open(os.path.join(args.output_png_dir, "input_images_all"), "wb") as input_files:
+ pickle.dump(list(input_images_all), input_files)
+
+ except tf.errors.OutOfRangeError:
+ break
+
+ feed_dict = {input_ph: input_results[name] for name, input_ph in input_phs.items()}
+ for stochastic_sample_ind in range(args.num_stochastic_samples):
+ gen_images = sess.run(model.outputs['gen_images'], feed_dict = feed_dict)
+ gen_images_stochastic.append(gen_images)
+ print("Stochastic_sample,", stochastic_sample_ind)
+ for i in range(args.batch_size):
+ print("batch", i)
+ #colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+ cmap_name = 'my_list'
+ if sample_ind < 20 and i == 1:
+ name = 'Stochastic_id_' + str(stochastic_sample_ind) + 'Batch_id_' + str(
+ sample_ind) + " + Sample_" + str(i)
+ gen_images_ = np.array(list(input_images[i,:context_frames]) + list(gen_images[i,-future_length:, :]))
+ #gen_images_ = gen_images[i, :]
+ input_images_ = input_images[i, :]
+ input_gen_diff = (input_images_[:, :, :,0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922) - (gen_images_[:, :, :, 0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922)
+
+ gen_mse_avg_ = [np.mean(input_gen_diff[frame, :, :] ** 2) for frame in
+ range(sequence_length)] # return the list with 10 (sequence) mse
+
+ fig = plt.figure(figsize=(18,6))
+ gs = gridspec.GridSpec(1, 10)
+ gs.update(wspace = 0., hspace = 0.)
+ ts = [0,5,9,10,12,14,16,18,19]
+ xlables = [round(i,2) for i in list(np.linspace(np.min(lon),np.max(lon),5))]
+ ylabels = [round(i,2) for i in list(np.linspace(np.max(lat),np.min(lat),5))]
+
+ for t in range(len(ts)):
+ #if t==0 : ax1=plt.subplot(gs[t])
+ ax1 = plt.subplot(gs[t])
+ input_image = input_images_[ts[t], :, :, 0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922
+ plt.imshow(input_image, cmap = 'jet', vmin=270, vmax=300)
+ ax1.title.set_text("t = " + str(ts[t]+1))
+ plt.setp([ax1], xticks = [], xticklabels = [], yticks = [], yticklabels = [])
+
+ if t == 0:
+ plt.setp([ax1], xticks = list(np.linspace(0, 64, 3)), xticklabels = xlables, yticks = list(np.linspace(0, 64, 3)), yticklabels = ylabels)
+ plt.ylabel("Ground Truth", fontsize=10)
+ plt.savefig(os.path.join(args.output_png_dir, "Ground_Truth_Sample_" + str(name) + ".jpg"))
+ plt.clf()
+
+ fig = plt.figure(figsize=(12,6))
+ gs = gridspec.GridSpec(1, 10)
+ gs.update(wspace = 0., hspace = 0.)
+ ts = [10,12,14,16,18,19]
+ for t in range(len(ts)):
+ #if t==0 : ax1=plt.subplot(gs[t])
+ ax1 = plt.subplot(gs[t])
+ gen_image = gen_images_[ts[t], :, :, 0] * (321.46630859375 - 235.2141571044922) + 235.2141571044922
+ plt.imshow(gen_image, cmap = 'jet', vmin=270, vmax=300)
+ ax1.title.set_text("t = " + str(ts[t]+1))
+ plt.setp([ax1], xticks = [], xticklabels = [], yticks = [], yticklabels = [])
+
+ plt.savefig(os.path.join(args.output_png_dir, "Predicted_Sample_" + str(name) + ".jpg"))
+ plt.clf()
+
+ # fig = plt.figure()
+ # gs = gridspec.GridSpec(4,6)
+ # gs.update(wspace = 0.7,hspace=0.8)
+ # ax1 = plt.subplot(gs[0:2,0:3])
+ # ax2 = plt.subplot(gs[0:2,3:],sharey=ax1)
+ # ax3 = plt.subplot(gs[2:4,0:3])
+ # ax4 = plt.subplot(gs[2:4,3:])
+ # xlables = [round(i,2) for i in list(np.linspace(np.min(lon),np.max(lon),5))]
+ # ylabels = [round(i,2) for i in list(np.linspace(np.max(lat),np.min(lat),5))]
+ # plt.setp([ax1,ax2,ax3],xticks=list(np.linspace(0,64,5)), xticklabels=xlables ,yticks=list(np.linspace(0,64,5)),yticklabels=ylabels)
+ # ax1.title.set_text("(a) Ground Truth")
+ # ax2.title.set_text("(b) SAVP")
+ # ax3.title.set_text("(c) Diff.")
+ # ax4.title.set_text("(d) MSE")
+ #
+ # ax1.xaxis.set_tick_params(labelsize=7)
+ # ax1.yaxis.set_tick_params(labelsize = 7)
+ # ax2.xaxis.set_tick_params(labelsize=7)
+ # ax2.yaxis.set_tick_params(labelsize = 7)
+ # ax3.xaxis.set_tick_params(labelsize=7)
+ # ax3.yaxis.set_tick_params(labelsize = 7)
+ #
+ # init_images = np.zeros((input_images_.shape[1], input_images_.shape[2]))
+ # print("inti images shape", init_images.shape)
+ # xdata, ydata = [], []
+ # #plot1 = ax1.imshow(init_images, cmap='jet', vmin =0, vmax = 1)
+ # #plot2 = ax2.imshow(init_images, cmap='jet', vmin =0, vmax = 1)
+ # plot1 = ax1.imshow(init_images, cmap='jet', vmin = 270, vmax = 300)
+ # plot2 = ax2.imshow(init_images, cmap='jet', vmin = 270, vmax = 300)
+ # #x = np.linspace(0, 64, 64)
+ # #y = np.linspace(0, 64, 64)
+ # #plot1 = ax1.contourf(x,y,init_images, cmap='jet', vmin = np.min(input_images), vmax = np.max(input_images))
+ # #plot2 = ax2.contourf(x,y,init_images, cmap='jet', vmin = np.min(input_images), vmax = np.max(input_images))
+ # fig.colorbar(plot1, ax=ax1).ax.tick_params(labelsize=7)
+ # fig.colorbar(plot2, ax=ax2).ax.tick_params(labelsize=7)
+ #
+ # cm = LinearSegmentedColormap.from_list(
+ # cmap_name, "bwr", N = 5)
+ #
+ # plot3 = ax3.imshow(init_images, vmin=-20, vmax=20, cmap=cm)#cmap = 'PuBu_r',
+ # #plot3 = ax3.imshow(init_images, vmin = -1, vmax = 1, cmap = cm) # cmap = 'PuBu_r',
+ # plot4, = ax4.plot([], [], color = "r")
+ # ax4.set_xlim(0, future_length-1)
+ # ax4.set_ylim(0, 20)
+ # #ax4.set_ylim(0, 0.5)
+ # ax4.set_xlabel("Frames", fontsize=10)
+ # #ax4.set_ylabel("MSE", fontsize=10)
+ # ax4.xaxis.set_tick_params(labelsize=7)
+ # ax4.yaxis.set_tick_params(labelsize=7)
+ #
+ #
+ # plots = [plot1, plot2, plot3, plot4]
+ #
+ # #fig.colorbar(plots[1], ax = [ax1, ax2])
+ #
+ # fig.colorbar(plots[2], ax=ax3).ax.tick_params(labelsize=7)
+ # #fig.colorbar(plot1[0], ax=ax1).ax.tick_params(labelsize=7)
+ # #fig.colorbar(plot2[1], ax=ax2).ax.tick_params(labelsize=7)
+ #
+ # def animation_sample(t):
+ # input_image = input_images_[t, :, :, 0]* (321.46630859375-235.2141571044922) + 235.2141571044922
+ # gen_image = gen_images_[t, :, :, 0]* (321.46630859375-235.2141571044922) + 235.2141571044922
+ # diff_image = input_gen_diff[t,:,:]
+ # # p = sns.lineplot(x=x,y=data,color="b")
+ # # p.tick_params(labelsize=17)
+ # # plt.setp(p.lines, linewidth=6)
+ # plots[0].set_data(input_image)
+ # plots[1].set_data(gen_image)
+ # #plots[0] = ax1.contourf(x, y, input_image, cmap = 'jet', vmin = np.min(input_images),vmax = np.max(input_images))
+ # #plots[1] = ax2.contourf(x, y, gen_image, cmap = 'jet', vmin = np.min(input_images),vmax = np.max(input_images))
+ # plots[2].set_data(diff_image)
+ #
+ # if t >= future_length:
+ # #data = gen_mse_avg_[:t + 1]
+ # # x = list(range(len(gen_mse_avg_)))[:t+1]
+ # xdata.append(t-future_length)
+ # print("xdata", xdata)
+ # ydata.append(gen_mse_avg_[t])
+ # print("ydata", ydata)
+ # plots[3].set_data(xdata, ydata)
+ # fig.suptitle("Predicted Frame " + str(t-future_length))
+ # else:
+ # #plots[3].set_data(xdata, ydata)
+ # fig.suptitle("Context Frame " + str(t))
+ # return plots
+ #
+ # ani = animation.FuncAnimation(fig, animation_sample, frames=len(gen_mse_avg_), interval = 1000,
+ # repeat_delay=2000)
+ # ani.save(os.path.join(args.output_png_dir, "Sample_" + str(name) + ".mp4"))
+
+ else:
+ pass
+
+
+
+ if sample_ind == 0:
+ gen_images_all = gen_images_stochastic
+ else:
+ gen_images_all = np.concatenate((np.array(gen_images_all), np.array(gen_images_stochastic)), axis=1)
+
+ if args.num_stochastic_samples == 1:
+ with open(os.path.join(args.output_png_dir, "gen_images_all"), "wb") as gen_files:
+ pickle.dump(list(gen_images_all[0]), gen_files)
+ else:
+ with open(os.path.join(args.output_png_dir, "gen_images_sample_id_" + str(sample_ind)),"wb") as gen_files:
+ pickle.dump(list(gen_images_stochastic), gen_files)
+ with open(os.path.join(args.output_png_dir, "gen_images_all_stochastic"), "wb") as gen_files:
+ pickle.dump(list(gen_images_all), gen_files)
+
+
+
+
+ sample_ind += args.batch_size
# # for i, gen_mse_avg_ in enumerate(gen_mse_avg):
@@ -390,8 +390,7 @@ def main():
# # plt.xlabel("Frames")
# # plt.ylabel("MSE_AVG")
# # #X = list(range(len(gen_mse_avg_)))
- # # #for t, gen_mse_avg_ in enume
- # rate(gen_mse_avg):
+ # # #for t, gen_mse_avg_ in enumerate(gen_mse_avg):
# # def animate_metric(j):
# # data = gen_mse_avg_[:(j+1)]
# # x = list(range(len(gen_mse_avg_)))[:(j+1)]