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Author: chao-tan

Diff for: data/sample.png

@@ -0,0 +1,179 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import os
+import random
+import cv2
+
+
+def load_mnist(traing_num=50000):
+    dat = np.load("data/mnist.npz")
+    X = dat['x_train'][:traing_num]
+    Y = dat['y_train'][:traing_num]
+    X_test = dat['x_test']
+    Y_test = dat['y_test']
+    Y = Y.reshape((Y.shape[0],))
+    Y_test = Y_test.reshape((Y_test.shape[0],))
+    return X, Y, X_test, Y_test
+
+
+def move_step(v0, p0, bounding_box):
+    xmin, xmax, ymin, ymax = bounding_box
+    assert (p0[0]>=xmin) and (p0[0]<=xmax) and (p0[1]>=ymin) and (p0[1]<=ymax)
+    v = v0.copy()
+    assert v[0] != 0.0 and v[1] != 0.0
+    p = v0 + p0
+    while (p[0]<xmin) or (p[0]>xmax) or (p[1]<ymin) or (p[1]>ymax):
+        vx, vy = v
+        x, y = p
+        dist = np.zeros((4,))
+        dist[0] = abs(x-xmin) if ymin <= (xmin-x)*vy/vx+y<=ymax else np.inf
+        dist[1] = abs(x-xmax) if ymin <= (xmax-x)*vy/vx+y<=ymax else np.inf
+        dist[2] = abs((y-ymin)*vx/vy) if xmin <= (ymin-y)*vx/vy+x<=xmax else np.inf
+        dist[3] = abs((y-ymax)*vx/vy) if xmin <= (ymax-y)*vx/vy+x<=xmax else np.inf
+        n = np.argmin(dist)
+        if n == 0:
+            v[0] = -v[0]
+            p[0] = 2*xmin-p[0]
+        elif n == 1:
+            v[0] = -v[0]
+            p[0] = 2*xmax-p[0]
+        elif n == 2:
+            v[1] = -v[1]
+            p[1] = 2*ymin-p[1]
+        elif n == 3:
+            v[1] = -v[1]
+            p[1] = 2*ymax-p[1]
+        else:
+            assert False
+    return v, p
+
+
+
+class MovingMNISTIterator(object):
+    def __init__(self):
+        self.mnist_train_img, self.mnist_train_label,self.mnist_test_img, self.mnist_test_label = load_mnist()
+
+    def sample(self, digitnum,
+               width,
+               height,
+               seqlen,
+               batch_size,
+               index_range=(0, 50000)):
+        """"""
+        """
+
+        :param digitnum: The num of the digits
+        :param width: The width of the generated images
+        :param height: The height of the generated images
+        :param seqlen: The length of the image sequence
+        :param index_range: by default
+        :return:
+        """
+        character_indices = np.random.randint(low=index_range[0], high=index_range[1],size=(batch_size, digitnum))
+        angles = np.random.random((batch_size, digitnum)) * (2 * np.pi)
+        magnitudes = np.random.random((batch_size, digitnum)) * (5 - 3) + 3
+        velocities = np.zeros((batch_size, digitnum, 2), dtype='float32')
+        velocities[..., 0] = magnitudes * np.cos(angles)
+        velocities[..., 1] = magnitudes * np.sin(angles)
+        xmin = 14.0
+        xmax = float(width) - 14.0
+        ymin = 14.0
+        ymax = float(height) - 14.0
+        positions = np.random.uniform(low=xmin, high=xmax,size=(batch_size, digitnum, 2))
+        seq = np.zeros((seqlen, batch_size, 1, height, width), dtype='uint8')
+        for i in range(batch_size):
+            for j in range(digitnum):
+                ind = character_indices[i, j]
+                v = velocities[i, j, :]
+                p = positions[i, j, :]
+                img = self.mnist_train_img[ind].reshape((28, 28))
+                for k in range(seqlen):
+                    topleft_y = int(p[0] - img.shape[0] / 2)
+                    topleft_x = int(p[1] - img.shape[1] / 2)
+                    seq[k, i, 0, topleft_y:topleft_y + 28, topleft_x:topleft_x + 28] = np.maximum(seq[k, i, 0, topleft_y:topleft_y + 28, topleft_x:topleft_x + 28],img)
+                    v, p = move_step(v, p, [xmin, xmax, ymin, ymax])
+        return seq
+
+
+
+class MovingMnist_Generation(object):
+    def __init__(self,digtnum, width, height, seq_length):
+        self.digtnum = digtnum
+        self.width = width
+        self.height = height
+        self.seq_length = seq_length
+
+    def next_batch(self,batch_size,next_seqlen=1,return_one=True,norm=False):
+        movingmnist = MovingMNISTIterator()
+
+
+        sample =  movingmnist.sample(digitnum=self.digtnum,
+                                     width=self.width,
+                                     height=self.height,
+                                     seqlen=self.seq_length+next_seqlen,
+                                     batch_size=batch_size)
+        sample = np.transpose(sample,(1,0,2,3,4))
+
+
+        x_batch = sample[:,0:self.seq_length,:,:,:]
+        y_batch = sample[:,self.seq_length:(self.seq_length+next_seqlen),:,:,:]
+
+        if return_one is True and next_seqlen == 1:
+            y_batch = np.reshape(y_batch,(batch_size,1,self.width,self.height))
+
+        # return the x_batch with shape(batchsize,seq_length,channels,width,height)
+        # return the y_batch with shape(batchsize,seq_length,channels,width,height) or (batchsize,channels,width,height) when y_batch has only one timestep
+        if norm:
+            return x_batch/255.0 , y_batch/255.0
+        else:
+            return x_batch,y_batch
+
+
+
+class SCMD_Generation(object):
+    def __init__(self,seq_length=5,next_seq=1,isTrain=True,return_one=True,norm=False,baseline=False):
+        self.seq_length = seq_length    # the length of the squence for training
+        self.next_seq = next_seq
+        self.is_train = isTrain
+        self.return_one = return_one
+        self.norm = norm
+        self.baseline = baseline
+        self.train_root = "data/SCMD2016/TRAIN"
+        self.test_root = "data/SCMD2016/TEST"
+        self.data_length = 0
+
+
+    def next_batch(self,batchsize):
+        x_batch = np.ndarray(shape=(batchsize,self.seq_length,1,200,200),dtype=np.float32)
+        y_batch = np.ndarray(shape=(batchsize,1,1,200,200),dtype=np.float32)
+
+        if self.is_train:
+            datalist = os.listdir(self.train_root)
+        else:
+            datalist = os.listdir(self.test_root)
+        self.data_length = len(datalist)
+
+        random_order = random.sample(range(1,self.data_length),batchsize)
+        if self.is_train:
+            root_path = self.train_root
+        else:
+            root_path = self.test_root
+
+        for i in range(batchsize):
+            for k in range(self.seq_length):
+                x_batch[i,k,0,:,:] = cv2.imread(root_path+"/SCMD_"+str(random_order[i])+"/"+str(k+1)+".png",cv2.IMREAD_GRAYSCALE)
+            y_batch[i,0,0,:,:] = cv2.imread(root_path+"/SCMD_"+str(random_order[i])+"/"+str(self.seq_length+1)+".png",cv2.IMREAD_GRAYSCALE)
+
+        x_batch,y_batch = x_batch*10.0,y_batch*10.0
+
+        if self.baseline:
+            x_batch = x_batch[:,:,:,100,100]
+            y_batch = y_batch[:,:,:,100,100]
+
+        if self.norm:
+            x_batch = x_batch/255.0
+            y_batch = y_batch/255.0
+
+
+        return x_batch,y_batch
+

Diff for: dataset.py

@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+import torch
+import dataset
+import cv2
+
+model_clstm_m = "checkpoint/clstm_m/model_best.pth"
+model_clstm_s = "checkpoint/clstm_s/model_best.pth"
+mdoel_forecast_clstm_m = "checkpoint/forecast_clstm_m/model_best.pth"
+mdoel_forecast_clstm_s = "checkpoint/forecast_clstm_s/model_best.pth"
+model_forecast_clstm_forecaster="checkpoint/forecast_clstm_forecaster/model_best.pth"
+
+
+
+def demo_mnist(model_path):
+    model = torch.load(model_path)
+    mnist = dataset.MovingMnist_Generation(digtnum=2,
+                                           width=64,
+                                           height=64,
+                                           seq_length=9)
+    x_batch,y_batch = mnist.next_batch(batch_size=1,
+                                       next_seqlen=1,
+                                       return_one=False,
+                                       norm=False)
+    x_batch = torch.from_numpy(x_batch).float()
+
+    output = model.forward(x_batch)
+    output = output.detach().cpu().numpy()
+    cv2.imwrite("demo_mnist.png",output[0][0][0])
+
+
+
+def demo_scmd(model_path):
+    model = torch.load(model_path)
+    scmd = dataset.SCDMD_Generation()
+    x_batch,y_batch = scmd.next_batch(batchsize=1)
+    x_batch = torch.from_numpy(x_batch).float()
+
+    output = model.forward(x_batch)
+    output = output.detach().cpu().numpy()
+    cv2.imwrite("demo_scmd.png",output[0][0][0])